Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
126 tokens/sec
GPT-4o
28 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

BDIS-SLAM: A lightweight CPU-based dense stereo SLAM for surgery (2312.15679v1)

Published 25 Dec 2023 in cs.RO and cs.CV

Abstract: Purpose: Common dense stereo Simultaneous Localization and Mapping (SLAM) approaches in Minimally Invasive Surgery (MIS) require high-end parallel computational resources for real-time implementation. Yet, it is not always feasible since the computational resources should be allocated to other tasks like segmentation, detection, and tracking. To solve the problem of limited parallel computational power, this research aims at a lightweight dense stereo SLAM system that works on a single-core CPU and achieves real-time performance (more than 30 Hz in typical scenarios). Methods: A new dense stereo mapping module is integrated with the ORB-SLAM2 system and named BDIS-SLAM. Our new dense stereo mapping module includes stereo matching and 3D dense depth mosaic methods. Stereo matching is achieved with the recently proposed CPU-level real-time matching algorithm Bayesian Dense Inverse Searching (BDIS). A BDIS-based shape recovery and a depth mosaic strategy are integrated as a new thread and coupled with the backbone ORB-SLAM2 system for real-time stereo shape recovery. Results: Experiments on in-vivo data sets show that BDIS-SLAM runs at over 30 Hz speed on modern single-core CPU in typical endoscopy/colonoscopy scenarios. BDIS-SLAM only consumes around an additional 12% time compared with the backbone ORB-SLAM2. Although our lightweight BDIS-SLAM simplifies the process by ignoring deformation and fusion procedures, it can provide a usable dense mapping for modern MIS on computationally constrained devices. Conclusion: The proposed BDIS-SLAM is a lightweight stereo dense SLAM system for MIS. It achieves 30 Hz on a modern single-core CPU in typical endoscopy/colonoscopy scenarios (image size around 640*480). BDIS-SLAM provides a low-cost solution for dense mapping in MIS and has the potential to be applied in surgical robots and AR systems.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (45)
  1. Widya, A.R., Monno, Y., Imahori, K., Okutomi, M., Suzuki, S., Gotoda, T., Miki, K.: 3D reconstruction of whole stomach from endoscope video using structure-from-motion. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3900–3904 (2019). IEEE (3) Portalés, C., Gimeno, J., Salvador, A., García-Fadrique, A., Casas-Yrurzum, S.: Mixed reality annotation of robotic-assisted surgery videos with real-time tracking and stereo matching. Computers & Graphics (2022) (4) Ratheesh, A., Soman, P., Nair, M.R., Devika, R., Aneesh, R.: Advanced algorithm for polyp detection using depth segmentation in colon endoscopy. In: 2016 International Conference on Communication Systems and Networks (ComNet), pp. 179–183 (2016). IEEE (5) Mahmood, F., Yang, Z., Chen, R., Borders, D., Xu, W., Durr, N.J.: Polyp segmentation and classification using predicted depth from monocular endoscopy. In: Medical Imaging 2019: Computer-Aided Diagnosis, vol. 10950, p. 1095011 (2019). International Society for Optics and Photonics (6) Jia, X., Mai, X., Cui, Y., Yuan, Y., Xing, X., Seo, H., Xing, L., Meng, M.Q.-H.: Automatic polyp recognition in colonoscopy images using deep learning and two-stage pyramidal feature prediction. IEEE Trans. Autom. Sci. Eng. 17(3), 1570–1584 (2020) (7) Ma, X., Song, C., Chiu, P.W., Li, Z.: Visual servo of a 6-DOF robotic stereo flexible endoscope based on da vinci research kit (dVRK) system. IEEE Robot. and Autom. Lett. 5(2), 820–827 (2020) (8) Ma, X., Song, C., Qian, L., Liu, W., Chiu, P.W., Li, Z.: Augmented reality-assisted autonomous view adjustment of a 6-DOF robotic stereo flexible endoscope 4(2), 356–367 (2022) (9) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Portalés, C., Gimeno, J., Salvador, A., García-Fadrique, A., Casas-Yrurzum, S.: Mixed reality annotation of robotic-assisted surgery videos with real-time tracking and stereo matching. Computers & Graphics (2022) (4) Ratheesh, A., Soman, P., Nair, M.R., Devika, R., Aneesh, R.: Advanced algorithm for polyp detection using depth segmentation in colon endoscopy. In: 2016 International Conference on Communication Systems and Networks (ComNet), pp. 179–183 (2016). IEEE (5) Mahmood, F., Yang, Z., Chen, R., Borders, D., Xu, W., Durr, N.J.: Polyp segmentation and classification using predicted depth from monocular endoscopy. In: Medical Imaging 2019: Computer-Aided Diagnosis, vol. 10950, p. 1095011 (2019). International Society for Optics and Photonics (6) Jia, X., Mai, X., Cui, Y., Yuan, Y., Xing, X., Seo, H., Xing, L., Meng, M.Q.-H.: Automatic polyp recognition in colonoscopy images using deep learning and two-stage pyramidal feature prediction. IEEE Trans. Autom. Sci. Eng. 17(3), 1570–1584 (2020) (7) Ma, X., Song, C., Chiu, P.W., Li, Z.: Visual servo of a 6-DOF robotic stereo flexible endoscope based on da vinci research kit (dVRK) system. IEEE Robot. and Autom. Lett. 5(2), 820–827 (2020) (8) Ma, X., Song, C., Qian, L., Liu, W., Chiu, P.W., Li, Z.: Augmented reality-assisted autonomous view adjustment of a 6-DOF robotic stereo flexible endoscope 4(2), 356–367 (2022) (9) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Ratheesh, A., Soman, P., Nair, M.R., Devika, R., Aneesh, R.: Advanced algorithm for polyp detection using depth segmentation in colon endoscopy. In: 2016 International Conference on Communication Systems and Networks (ComNet), pp. 179–183 (2016). IEEE (5) Mahmood, F., Yang, Z., Chen, R., Borders, D., Xu, W., Durr, N.J.: Polyp segmentation and classification using predicted depth from monocular endoscopy. In: Medical Imaging 2019: Computer-Aided Diagnosis, vol. 10950, p. 1095011 (2019). International Society for Optics and Photonics (6) Jia, X., Mai, X., Cui, Y., Yuan, Y., Xing, X., Seo, H., Xing, L., Meng, M.Q.-H.: Automatic polyp recognition in colonoscopy images using deep learning and two-stage pyramidal feature prediction. IEEE Trans. Autom. Sci. Eng. 17(3), 1570–1584 (2020) (7) Ma, X., Song, C., Chiu, P.W., Li, Z.: Visual servo of a 6-DOF robotic stereo flexible endoscope based on da vinci research kit (dVRK) system. IEEE Robot. and Autom. Lett. 5(2), 820–827 (2020) (8) Ma, X., Song, C., Qian, L., Liu, W., Chiu, P.W., Li, Z.: Augmented reality-assisted autonomous view adjustment of a 6-DOF robotic stereo flexible endoscope 4(2), 356–367 (2022) (9) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmood, F., Yang, Z., Chen, R., Borders, D., Xu, W., Durr, N.J.: Polyp segmentation and classification using predicted depth from monocular endoscopy. In: Medical Imaging 2019: Computer-Aided Diagnosis, vol. 10950, p. 1095011 (2019). International Society for Optics and Photonics (6) Jia, X., Mai, X., Cui, Y., Yuan, Y., Xing, X., Seo, H., Xing, L., Meng, M.Q.-H.: Automatic polyp recognition in colonoscopy images using deep learning and two-stage pyramidal feature prediction. IEEE Trans. Autom. Sci. Eng. 17(3), 1570–1584 (2020) (7) Ma, X., Song, C., Chiu, P.W., Li, Z.: Visual servo of a 6-DOF robotic stereo flexible endoscope based on da vinci research kit (dVRK) system. IEEE Robot. and Autom. Lett. 5(2), 820–827 (2020) (8) Ma, X., Song, C., Qian, L., Liu, W., Chiu, P.W., Li, Z.: Augmented reality-assisted autonomous view adjustment of a 6-DOF robotic stereo flexible endoscope 4(2), 356–367 (2022) (9) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Jia, X., Mai, X., Cui, Y., Yuan, Y., Xing, X., Seo, H., Xing, L., Meng, M.Q.-H.: Automatic polyp recognition in colonoscopy images using deep learning and two-stage pyramidal feature prediction. IEEE Trans. Autom. Sci. Eng. 17(3), 1570–1584 (2020) (7) Ma, X., Song, C., Chiu, P.W., Li, Z.: Visual servo of a 6-DOF robotic stereo flexible endoscope based on da vinci research kit (dVRK) system. IEEE Robot. and Autom. Lett. 5(2), 820–827 (2020) (8) Ma, X., Song, C., Qian, L., Liu, W., Chiu, P.W., Li, Z.: Augmented reality-assisted autonomous view adjustment of a 6-DOF robotic stereo flexible endoscope 4(2), 356–367 (2022) (9) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Ma, X., Song, C., Chiu, P.W., Li, Z.: Visual servo of a 6-DOF robotic stereo flexible endoscope based on da vinci research kit (dVRK) system. IEEE Robot. and Autom. Lett. 5(2), 820–827 (2020) (8) Ma, X., Song, C., Qian, L., Liu, W., Chiu, P.W., Li, Z.: Augmented reality-assisted autonomous view adjustment of a 6-DOF robotic stereo flexible endoscope 4(2), 356–367 (2022) (9) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Ma, X., Song, C., Qian, L., Liu, W., Chiu, P.W., Li, Z.: Augmented reality-assisted autonomous view adjustment of a 6-DOF robotic stereo flexible endoscope 4(2), 356–367 (2022) (9) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  2. Portalés, C., Gimeno, J., Salvador, A., García-Fadrique, A., Casas-Yrurzum, S.: Mixed reality annotation of robotic-assisted surgery videos with real-time tracking and stereo matching. Computers & Graphics (2022) (4) Ratheesh, A., Soman, P., Nair, M.R., Devika, R., Aneesh, R.: Advanced algorithm for polyp detection using depth segmentation in colon endoscopy. In: 2016 International Conference on Communication Systems and Networks (ComNet), pp. 179–183 (2016). IEEE (5) Mahmood, F., Yang, Z., Chen, R., Borders, D., Xu, W., Durr, N.J.: Polyp segmentation and classification using predicted depth from monocular endoscopy. In: Medical Imaging 2019: Computer-Aided Diagnosis, vol. 10950, p. 1095011 (2019). International Society for Optics and Photonics (6) Jia, X., Mai, X., Cui, Y., Yuan, Y., Xing, X., Seo, H., Xing, L., Meng, M.Q.-H.: Automatic polyp recognition in colonoscopy images using deep learning and two-stage pyramidal feature prediction. IEEE Trans. Autom. Sci. Eng. 17(3), 1570–1584 (2020) (7) Ma, X., Song, C., Chiu, P.W., Li, Z.: Visual servo of a 6-DOF robotic stereo flexible endoscope based on da vinci research kit (dVRK) system. IEEE Robot. and Autom. Lett. 5(2), 820–827 (2020) (8) Ma, X., Song, C., Qian, L., Liu, W., Chiu, P.W., Li, Z.: Augmented reality-assisted autonomous view adjustment of a 6-DOF robotic stereo flexible endoscope 4(2), 356–367 (2022) (9) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Ratheesh, A., Soman, P., Nair, M.R., Devika, R., Aneesh, R.: Advanced algorithm for polyp detection using depth segmentation in colon endoscopy. In: 2016 International Conference on Communication Systems and Networks (ComNet), pp. 179–183 (2016). IEEE (5) Mahmood, F., Yang, Z., Chen, R., Borders, D., Xu, W., Durr, N.J.: Polyp segmentation and classification using predicted depth from monocular endoscopy. In: Medical Imaging 2019: Computer-Aided Diagnosis, vol. 10950, p. 1095011 (2019). International Society for Optics and Photonics (6) Jia, X., Mai, X., Cui, Y., Yuan, Y., Xing, X., Seo, H., Xing, L., Meng, M.Q.-H.: Automatic polyp recognition in colonoscopy images using deep learning and two-stage pyramidal feature prediction. IEEE Trans. Autom. Sci. Eng. 17(3), 1570–1584 (2020) (7) Ma, X., Song, C., Chiu, P.W., Li, Z.: Visual servo of a 6-DOF robotic stereo flexible endoscope based on da vinci research kit (dVRK) system. IEEE Robot. and Autom. Lett. 5(2), 820–827 (2020) (8) Ma, X., Song, C., Qian, L., Liu, W., Chiu, P.W., Li, Z.: Augmented reality-assisted autonomous view adjustment of a 6-DOF robotic stereo flexible endoscope 4(2), 356–367 (2022) (9) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmood, F., Yang, Z., Chen, R., Borders, D., Xu, W., Durr, N.J.: Polyp segmentation and classification using predicted depth from monocular endoscopy. In: Medical Imaging 2019: Computer-Aided Diagnosis, vol. 10950, p. 1095011 (2019). International Society for Optics and Photonics (6) Jia, X., Mai, X., Cui, Y., Yuan, Y., Xing, X., Seo, H., Xing, L., Meng, M.Q.-H.: Automatic polyp recognition in colonoscopy images using deep learning and two-stage pyramidal feature prediction. IEEE Trans. Autom. Sci. Eng. 17(3), 1570–1584 (2020) (7) Ma, X., Song, C., Chiu, P.W., Li, Z.: Visual servo of a 6-DOF robotic stereo flexible endoscope based on da vinci research kit (dVRK) system. IEEE Robot. and Autom. Lett. 5(2), 820–827 (2020) (8) Ma, X., Song, C., Qian, L., Liu, W., Chiu, P.W., Li, Z.: Augmented reality-assisted autonomous view adjustment of a 6-DOF robotic stereo flexible endoscope 4(2), 356–367 (2022) (9) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Jia, X., Mai, X., Cui, Y., Yuan, Y., Xing, X., Seo, H., Xing, L., Meng, M.Q.-H.: Automatic polyp recognition in colonoscopy images using deep learning and two-stage pyramidal feature prediction. IEEE Trans. Autom. Sci. Eng. 17(3), 1570–1584 (2020) (7) Ma, X., Song, C., Chiu, P.W., Li, Z.: Visual servo of a 6-DOF robotic stereo flexible endoscope based on da vinci research kit (dVRK) system. IEEE Robot. and Autom. Lett. 5(2), 820–827 (2020) (8) Ma, X., Song, C., Qian, L., Liu, W., Chiu, P.W., Li, Z.: Augmented reality-assisted autonomous view adjustment of a 6-DOF robotic stereo flexible endoscope 4(2), 356–367 (2022) (9) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Ma, X., Song, C., Chiu, P.W., Li, Z.: Visual servo of a 6-DOF robotic stereo flexible endoscope based on da vinci research kit (dVRK) system. IEEE Robot. and Autom. Lett. 5(2), 820–827 (2020) (8) Ma, X., Song, C., Qian, L., Liu, W., Chiu, P.W., Li, Z.: Augmented reality-assisted autonomous view adjustment of a 6-DOF robotic stereo flexible endoscope 4(2), 356–367 (2022) (9) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Ma, X., Song, C., Qian, L., Liu, W., Chiu, P.W., Li, Z.: Augmented reality-assisted autonomous view adjustment of a 6-DOF robotic stereo flexible endoscope 4(2), 356–367 (2022) (9) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  3. Ratheesh, A., Soman, P., Nair, M.R., Devika, R., Aneesh, R.: Advanced algorithm for polyp detection using depth segmentation in colon endoscopy. In: 2016 International Conference on Communication Systems and Networks (ComNet), pp. 179–183 (2016). IEEE (5) Mahmood, F., Yang, Z., Chen, R., Borders, D., Xu, W., Durr, N.J.: Polyp segmentation and classification using predicted depth from monocular endoscopy. In: Medical Imaging 2019: Computer-Aided Diagnosis, vol. 10950, p. 1095011 (2019). International Society for Optics and Photonics (6) Jia, X., Mai, X., Cui, Y., Yuan, Y., Xing, X., Seo, H., Xing, L., Meng, M.Q.-H.: Automatic polyp recognition in colonoscopy images using deep learning and two-stage pyramidal feature prediction. IEEE Trans. Autom. Sci. Eng. 17(3), 1570–1584 (2020) (7) Ma, X., Song, C., Chiu, P.W., Li, Z.: Visual servo of a 6-DOF robotic stereo flexible endoscope based on da vinci research kit (dVRK) system. IEEE Robot. and Autom. Lett. 5(2), 820–827 (2020) (8) Ma, X., Song, C., Qian, L., Liu, W., Chiu, P.W., Li, Z.: Augmented reality-assisted autonomous view adjustment of a 6-DOF robotic stereo flexible endoscope 4(2), 356–367 (2022) (9) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmood, F., Yang, Z., Chen, R., Borders, D., Xu, W., Durr, N.J.: Polyp segmentation and classification using predicted depth from monocular endoscopy. In: Medical Imaging 2019: Computer-Aided Diagnosis, vol. 10950, p. 1095011 (2019). International Society for Optics and Photonics (6) Jia, X., Mai, X., Cui, Y., Yuan, Y., Xing, X., Seo, H., Xing, L., Meng, M.Q.-H.: Automatic polyp recognition in colonoscopy images using deep learning and two-stage pyramidal feature prediction. IEEE Trans. Autom. Sci. Eng. 17(3), 1570–1584 (2020) (7) Ma, X., Song, C., Chiu, P.W., Li, Z.: Visual servo of a 6-DOF robotic stereo flexible endoscope based on da vinci research kit (dVRK) system. IEEE Robot. and Autom. Lett. 5(2), 820–827 (2020) (8) Ma, X., Song, C., Qian, L., Liu, W., Chiu, P.W., Li, Z.: Augmented reality-assisted autonomous view adjustment of a 6-DOF robotic stereo flexible endoscope 4(2), 356–367 (2022) (9) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Jia, X., Mai, X., Cui, Y., Yuan, Y., Xing, X., Seo, H., Xing, L., Meng, M.Q.-H.: Automatic polyp recognition in colonoscopy images using deep learning and two-stage pyramidal feature prediction. IEEE Trans. Autom. Sci. Eng. 17(3), 1570–1584 (2020) (7) Ma, X., Song, C., Chiu, P.W., Li, Z.: Visual servo of a 6-DOF robotic stereo flexible endoscope based on da vinci research kit (dVRK) system. IEEE Robot. and Autom. Lett. 5(2), 820–827 (2020) (8) Ma, X., Song, C., Qian, L., Liu, W., Chiu, P.W., Li, Z.: Augmented reality-assisted autonomous view adjustment of a 6-DOF robotic stereo flexible endoscope 4(2), 356–367 (2022) (9) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Ma, X., Song, C., Chiu, P.W., Li, Z.: Visual servo of a 6-DOF robotic stereo flexible endoscope based on da vinci research kit (dVRK) system. IEEE Robot. and Autom. Lett. 5(2), 820–827 (2020) (8) Ma, X., Song, C., Qian, L., Liu, W., Chiu, P.W., Li, Z.: Augmented reality-assisted autonomous view adjustment of a 6-DOF robotic stereo flexible endoscope 4(2), 356–367 (2022) (9) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Ma, X., Song, C., Qian, L., Liu, W., Chiu, P.W., Li, Z.: Augmented reality-assisted autonomous view adjustment of a 6-DOF robotic stereo flexible endoscope 4(2), 356–367 (2022) (9) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  4. Mahmood, F., Yang, Z., Chen, R., Borders, D., Xu, W., Durr, N.J.: Polyp segmentation and classification using predicted depth from monocular endoscopy. In: Medical Imaging 2019: Computer-Aided Diagnosis, vol. 10950, p. 1095011 (2019). International Society for Optics and Photonics (6) Jia, X., Mai, X., Cui, Y., Yuan, Y., Xing, X., Seo, H., Xing, L., Meng, M.Q.-H.: Automatic polyp recognition in colonoscopy images using deep learning and two-stage pyramidal feature prediction. IEEE Trans. Autom. Sci. Eng. 17(3), 1570–1584 (2020) (7) Ma, X., Song, C., Chiu, P.W., Li, Z.: Visual servo of a 6-DOF robotic stereo flexible endoscope based on da vinci research kit (dVRK) system. IEEE Robot. and Autom. Lett. 5(2), 820–827 (2020) (8) Ma, X., Song, C., Qian, L., Liu, W., Chiu, P.W., Li, Z.: Augmented reality-assisted autonomous view adjustment of a 6-DOF robotic stereo flexible endoscope 4(2), 356–367 (2022) (9) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Jia, X., Mai, X., Cui, Y., Yuan, Y., Xing, X., Seo, H., Xing, L., Meng, M.Q.-H.: Automatic polyp recognition in colonoscopy images using deep learning and two-stage pyramidal feature prediction. IEEE Trans. Autom. Sci. Eng. 17(3), 1570–1584 (2020) (7) Ma, X., Song, C., Chiu, P.W., Li, Z.: Visual servo of a 6-DOF robotic stereo flexible endoscope based on da vinci research kit (dVRK) system. IEEE Robot. and Autom. Lett. 5(2), 820–827 (2020) (8) Ma, X., Song, C., Qian, L., Liu, W., Chiu, P.W., Li, Z.: Augmented reality-assisted autonomous view adjustment of a 6-DOF robotic stereo flexible endoscope 4(2), 356–367 (2022) (9) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Ma, X., Song, C., Chiu, P.W., Li, Z.: Visual servo of a 6-DOF robotic stereo flexible endoscope based on da vinci research kit (dVRK) system. IEEE Robot. and Autom. Lett. 5(2), 820–827 (2020) (8) Ma, X., Song, C., Qian, L., Liu, W., Chiu, P.W., Li, Z.: Augmented reality-assisted autonomous view adjustment of a 6-DOF robotic stereo flexible endoscope 4(2), 356–367 (2022) (9) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Ma, X., Song, C., Qian, L., Liu, W., Chiu, P.W., Li, Z.: Augmented reality-assisted autonomous view adjustment of a 6-DOF robotic stereo flexible endoscope 4(2), 356–367 (2022) (9) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  5. Jia, X., Mai, X., Cui, Y., Yuan, Y., Xing, X., Seo, H., Xing, L., Meng, M.Q.-H.: Automatic polyp recognition in colonoscopy images using deep learning and two-stage pyramidal feature prediction. IEEE Trans. Autom. Sci. Eng. 17(3), 1570–1584 (2020) (7) Ma, X., Song, C., Chiu, P.W., Li, Z.: Visual servo of a 6-DOF robotic stereo flexible endoscope based on da vinci research kit (dVRK) system. IEEE Robot. and Autom. Lett. 5(2), 820–827 (2020) (8) Ma, X., Song, C., Qian, L., Liu, W., Chiu, P.W., Li, Z.: Augmented reality-assisted autonomous view adjustment of a 6-DOF robotic stereo flexible endoscope 4(2), 356–367 (2022) (9) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Ma, X., Song, C., Chiu, P.W., Li, Z.: Visual servo of a 6-DOF robotic stereo flexible endoscope based on da vinci research kit (dVRK) system. IEEE Robot. and Autom. Lett. 5(2), 820–827 (2020) (8) Ma, X., Song, C., Qian, L., Liu, W., Chiu, P.W., Li, Z.: Augmented reality-assisted autonomous view adjustment of a 6-DOF robotic stereo flexible endoscope 4(2), 356–367 (2022) (9) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Ma, X., Song, C., Qian, L., Liu, W., Chiu, P.W., Li, Z.: Augmented reality-assisted autonomous view adjustment of a 6-DOF robotic stereo flexible endoscope 4(2), 356–367 (2022) (9) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  6. Ma, X., Song, C., Chiu, P.W., Li, Z.: Visual servo of a 6-DOF robotic stereo flexible endoscope based on da vinci research kit (dVRK) system. IEEE Robot. and Autom. Lett. 5(2), 820–827 (2020) (8) Ma, X., Song, C., Qian, L., Liu, W., Chiu, P.W., Li, Z.: Augmented reality-assisted autonomous view adjustment of a 6-DOF robotic stereo flexible endoscope 4(2), 356–367 (2022) (9) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Ma, X., Song, C., Qian, L., Liu, W., Chiu, P.W., Li, Z.: Augmented reality-assisted autonomous view adjustment of a 6-DOF robotic stereo flexible endoscope 4(2), 356–367 (2022) (9) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  7. Ma, X., Song, C., Qian, L., Liu, W., Chiu, P.W., Li, Z.: Augmented reality-assisted autonomous view adjustment of a 6-DOF robotic stereo flexible endoscope 4(2), 356–367 (2022) (9) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  8. Grasa, O.G., Civera, J., Montiel, J.: EKF monocular SLAM with relocalization for laparoscopic sequences. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 4816–4821 (2011). IEEE (10) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  9. Lin, B., Johnson, A., Qian, X., Sanchez, J., Sun, Y.: Simultaneous tracking, 3D reconstruction and deforming point detection for stereoscope guided surgery. In: Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, pp. 35–44 (2013). Springer (11) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  10. Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.: ORBSLAM-based endoscope tracking and 3D reconstruction. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 72–83 (2016). Springer (12) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  11. Mahmoud, N., Hostettler, A., Collins, T., Soler, L., Doignon, C., Montiel, J.M.M.: SLAM based quasi dense reconstruction for minimally invasive surgery scenes. arXiv preprint arXiv:1705.09107 (2017) (13) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  12. Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.: A non-rigid map fusion-based direct SLAM method for endoscopic capsule robots. International journal of intelligent robotics and applications 1(4), 399–409 (2017) (14) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  13. Oliva Maza, L., Steidle, F., Klodmann, J., Strobl, K., Triebel, R.: An ORB-SLAM3-based approach for surgical navigation in ureteroscopy. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–7 (2022) (15) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  14. Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: Dynamic reconstruction of deformable soft-tissue with stereo scope in minimal invasive surgery. IEEE Robot. and Autom. Lett. 3(1), 155–162 (2017) (16) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  15. Song, J., Wang, J., Zhao, L., Huang, S., Dissanayake, G.: MIS-SLAM: Real-time large-scale dense deformable SLAM system in minimal invasive surgery based on heterogeneous computing. IEEE Robot. and Autom. Lett. 3(4), 4068–4075 (2018) (17) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  16. Zhou, H., Jayender, J.: Real-time nonrigid mosaicking of laparoscopy images. IEEE Trans. Med. Imag. 40(6), 1726–1736 (2021) (18) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  17. Lamarca, J., Parashar, S., Bartoli, A., Montiel, J.: DefSLAM: Tracking and mapping of deforming scenes from monocular sequences. IEEE Trans. Robot. 37(1), 291–303 (2020) (19) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  18. Yang, Z., Lin, S., Simon, R., Linte, C.A.: Endoscope localization and dense surgical scene reconstruction for stereo endoscopy by unsupervised optical flow and kanade-lucas-tomasi tracking. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 4839–4842 (2022). IEEE (20) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  19. Liu, X., Li, Z., Ishii, M., Hager, G.D., Taylor, R.H., Unberath, M.: Sage: SLAM with appearance and geometry prior for endoscopy. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 5587–5593 (2022). IEEE (21) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  20. Whelan, T., Leutenegger, S., Salas-Moreno, R., Glocker, B., Davison, A.: ElasticFusion: Dense SLAM without a pose graph. (2015). Robotics: Science and Systems (22) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  21. Song, J., Zhu, Q., Lin, J., Ghaffari, M.: BDIS: Bayesian dense inverse searching method for real-time stereo surgical image matching. IEEE Trans. Robot., 1–19 (2022). https://doi.org/10.1109/TRO.2022.3215018 (23) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  22. Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian Conference on Computer Vision, pp. 25–38 (2010). Springer (24) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  23. Zampokas, G., Tsiolis, K., Peleka, G., Mariolis, I., Malasiotis, S., Tzovaras, D.: Real-time 3D reconstruction in minimally invasive surgery with quasi-dense matching. In: IEEE Int. Conf. on Imaging Sys. and Tech., pp. 1–6 (2018). IEEE (25) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  24. Cartucho, J., Tukra, S., Li, Y., S. Elson, D., Giannarou, S.: VisionBlender: a tool to efficiently generate computer vision datasets for robotic surgery. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–8 (2020) (26) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  25. Zhang, L., Ye, M., Giataganas, P., Hughes, M., Yang, G.-Z.: Autonomous scanning for endomicroscopic mosaicing and 3D fusion. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 3587–3593 (2017). IEEE (27) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  26. Zhan, J., Cartucho, J., Giannarou, S.: Autonomous tissue scanning under free-form motion for intraoperative tissue characterisation. In: Proc. IEEE Int. Conf. Robot. and Automation, pp. 11147–11154 (2020). IEEE (28) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  27. Chang, J.-R., Chen, Y.-S.: Pyramid stereo matching network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5410–5418 (2018) (29) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  28. Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 5515–5524 (2019) (30) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  29. Guo, X., Yang, K., Yang, W., Wang, X., Li, H.: Group-wise correlation stereo network. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 3273–3282 (2019) (31) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  30. Tonioni, A., Tosi, F., Poggi, M., Mattoccia, S., Stefano, L.D.: Real-time self-adaptive deep stereo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 195–204 (2019) (32) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  31. Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp. 1959–1968 (2020) (33) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  32. Brandao, P., Psychogyios, D., Mazomenos, E., Stoyanov, D., Janatka, M.: HAPNet: hierarchically aggregated pyramid network for real-time stereo matching. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1–6 (2020) (34) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  33. Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-DSSR: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 415–425 (2021). Springer (35) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  34. Allan, M., Mcleod, J., Wang, C.C., Rosenthal, J.C., Fu, K.X., Zeffiro, T., Xia, W., Zhanshi, Z., Luo, H., Zhang, X., et al.: Stereo correspondence and reconstruction of endoscopic data challenge. arXiv preprint arXiv:2101.01133 (2021) (36) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  35. Newcombe, R.A., Izadi, S., et al.: KinectFusion: Real-time dense surface mapping and tracking. In: IEEE Intern. Symposium on Mixed and Augment. Reality, pp. 127–136 (2011). IEEE (37) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  36. Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 559–568 (2011) (38) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  37. Newcombe, R.A., Fox, D., Seitz, S.M.: DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 343–352 (2015) (39) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  38. Song, J., Zhu, Q., Lin, J., Ghaffari, M.: Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery. In: Int. Conf. on Med. Image Comput. and Comput. Assist. Interv., pp. 333–344 (2022). Springer (40) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  39. Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM2 system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017) (41) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  40. Mahmoud, N., Collins, T., Hostettler, A., Soler, L., Doignon, C., Montiel, J.M.M.: Live tracking and dense reconstruction for handheld monocular endoscopy. IEEE transactions on medical imaging 38(1), 79–89 (2018) (42) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  41. Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1), 7–42 (2002) (43) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  42. Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004) (44) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  43. Kroeger, T., Timofte, R., Dai, D., Van Gool, L.: Fast optical flow using dense inverse search. In: Proc. European Conf. Comput. Vis., pp. 471–488 (2016). Springer (45) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  44. Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2017) (46) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013) Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
  45. Giannarou, S., Visentini-Scarzanella, M., Yang, G.-Z.: Probabilistic tracking of affine-invariant anisotropic regions. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 130–143 (2013)
Citations (1)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now