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Harnessing Deep Learning of Point Clouds for Inverse Control of 3D Shape Morphing (2401.15219v1)
Published 26 Jan 2024 in cs.RO, cs.SY, and eess.SY
Abstract: Shape-morphing devices, a crucial branch in soft robotics, hold significant application value in areas like human-machine interfaces, biomimetic robotics, and tools for interacting with biological systems. To achieve three-dimensional (3D) programmable shape morphing (PSM), the deployment of array-based actuators is essential. However, a critical knowledge gap impeding the development of 3D PSM is the challenge of controlling the complex systems formed by these soft actuator arrays. This study introduces a novel approach, for the first time, representing the configuration of shape morphing devices using point cloud data and employing deep learning to map these configurations to control inputs. We propose Shape Morphing Net (SMNet), a method that realizes the regression from point cloud data to high-dimensional continuous vectors. Applied to previous 2D PSM actuator arrays, SMNet significantly enhances control precision from 82.23% to 97.68%. Further, we extend its application to 3D PSM devices with three different actuator mechanisms, demonstrating the universal applicability of SMNet to the control of 3D shape morphing technologies. In our demonstrations, we confirm the efficacy of inverse control, where 3D PSM devices successfully replicate target shapes. These shapes are obtained either through 3D scanning of physical objects or via 3D modeling software. The results show that within the deformable range of 3D PSM devices, accurate reproduction of the desired shapes is achievable. The findings of this research represent a substantial advancement in soft robotics, particularly for applications demanding intricate 3D shape transformations, and establish a foundational framework for future developments in the field.
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R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Yu, X. et al. Skin-integrated wireless haptic interfaces for virtual and augmented reality. Nature 575, 473–479 (2019). [3] Peng, C. et al. Dynamically programmable surfaces for high-speed optical modulation and detection. Ph.D. thesis, Massachusetts Institute of Technology (2020). [4] Chen, X. et al. Harnessing 4d printing bioscaffolds for advanced orthopedics. Small 18, 2106824 (2022). [5] Kirillova, A. & Ionov, L. Shape-changing polymers for biomedical applications. Journal of Materials Chemistry B 7, 1597–1624 (2019). [6] Viola, J. M. et al. Guiding cell network assembly using shape-morphing hydrogels. Advanced Materials 32, 2002195 (2020). [7] Ford, M. J. et al. A multifunctional shape-morphing elastomer with liquid metal inclusions. Proceedings of the National Academy of Sciences 116, 21438–21444 (2019). [8] Kotikian, A., Truby, R. L., Boley, J. W., White, T. J. & Lewis, J. A. 3d printing of liquid crystal elastomeric actuators with spatially programed nematic order. Advanced materials 30, 1706164 (2018). [9] Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Peng, C. et al. Dynamically programmable surfaces for high-speed optical modulation and detection. Ph.D. thesis, Massachusetts Institute of Technology (2020). [4] Chen, X. et al. Harnessing 4d printing bioscaffolds for advanced orthopedics. Small 18, 2106824 (2022). [5] Kirillova, A. & Ionov, L. Shape-changing polymers for biomedical applications. Journal of Materials Chemistry B 7, 1597–1624 (2019). [6] Viola, J. M. et al. Guiding cell network assembly using shape-morphing hydrogels. Advanced Materials 32, 2002195 (2020). [7] Ford, M. J. et al. A multifunctional shape-morphing elastomer with liquid metal inclusions. Proceedings of the National Academy of Sciences 116, 21438–21444 (2019). [8] Kotikian, A., Truby, R. L., Boley, J. W., White, T. J. & Lewis, J. A. 3d printing of liquid crystal elastomeric actuators with spatially programed nematic order. Advanced materials 30, 1706164 (2018). [9] Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Chen, X. et al. Harnessing 4d printing bioscaffolds for advanced orthopedics. Small 18, 2106824 (2022). [5] Kirillova, A. & Ionov, L. Shape-changing polymers for biomedical applications. Journal of Materials Chemistry B 7, 1597–1624 (2019). [6] Viola, J. M. et al. Guiding cell network assembly using shape-morphing hydrogels. Advanced Materials 32, 2002195 (2020). [7] Ford, M. J. et al. A multifunctional shape-morphing elastomer with liquid metal inclusions. Proceedings of the National Academy of Sciences 116, 21438–21444 (2019). [8] Kotikian, A., Truby, R. L., Boley, J. W., White, T. J. & Lewis, J. A. 3d printing of liquid crystal elastomeric actuators with spatially programed nematic order. Advanced materials 30, 1706164 (2018). [9] Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kirillova, A. & Ionov, L. Shape-changing polymers for biomedical applications. Journal of Materials Chemistry B 7, 1597–1624 (2019). [6] Viola, J. M. et al. Guiding cell network assembly using shape-morphing hydrogels. Advanced Materials 32, 2002195 (2020). [7] Ford, M. J. et al. A multifunctional shape-morphing elastomer with liquid metal inclusions. Proceedings of the National Academy of Sciences 116, 21438–21444 (2019). [8] Kotikian, A., Truby, R. L., Boley, J. W., White, T. J. & Lewis, J. A. 3d printing of liquid crystal elastomeric actuators with spatially programed nematic order. Advanced materials 30, 1706164 (2018). [9] Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Viola, J. M. et al. Guiding cell network assembly using shape-morphing hydrogels. Advanced Materials 32, 2002195 (2020). [7] Ford, M. J. et al. A multifunctional shape-morphing elastomer with liquid metal inclusions. Proceedings of the National Academy of Sciences 116, 21438–21444 (2019). [8] Kotikian, A., Truby, R. L., Boley, J. W., White, T. J. & Lewis, J. A. 3d printing of liquid crystal elastomeric actuators with spatially programed nematic order. Advanced materials 30, 1706164 (2018). [9] Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ford, M. J. et al. A multifunctional shape-morphing elastomer with liquid metal inclusions. Proceedings of the National Academy of Sciences 116, 21438–21444 (2019). [8] Kotikian, A., Truby, R. L., Boley, J. W., White, T. J. & Lewis, J. A. 3d printing of liquid crystal elastomeric actuators with spatially programed nematic order. Advanced materials 30, 1706164 (2018). [9] Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kotikian, A., Truby, R. L., Boley, J. W., White, T. J. & Lewis, J. A. 3d printing of liquid crystal elastomeric actuators with spatially programed nematic order. Advanced materials 30, 1706164 (2018). [9] Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. 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[52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. 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Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. 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Advanced materials 30, 1706164 (2018). [9] Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Peng, C. et al. Dynamically programmable surfaces for high-speed optical modulation and detection. Ph.D. thesis, Massachusetts Institute of Technology (2020). [4] Chen, X. et al. Harnessing 4d printing bioscaffolds for advanced orthopedics. Small 18, 2106824 (2022). [5] Kirillova, A. & Ionov, L. Shape-changing polymers for biomedical applications. Journal of Materials Chemistry B 7, 1597–1624 (2019). [6] Viola, J. M. et al. Guiding cell network assembly using shape-morphing hydrogels. Advanced Materials 32, 2002195 (2020). [7] Ford, M. J. et al. A multifunctional shape-morphing elastomer with liquid metal inclusions. Proceedings of the National Academy of Sciences 116, 21438–21444 (2019). [8] Kotikian, A., Truby, R. L., Boley, J. W., White, T. J. & Lewis, J. A. 3d printing of liquid crystal elastomeric actuators with spatially programed nematic order. Advanced materials 30, 1706164 (2018). [9] Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Chen, X. et al. Harnessing 4d printing bioscaffolds for advanced orthopedics. Small 18, 2106824 (2022). [5] Kirillova, A. & Ionov, L. Shape-changing polymers for biomedical applications. Journal of Materials Chemistry B 7, 1597–1624 (2019). [6] Viola, J. M. et al. Guiding cell network assembly using shape-morphing hydrogels. Advanced Materials 32, 2002195 (2020). [7] Ford, M. J. et al. A multifunctional shape-morphing elastomer with liquid metal inclusions. Proceedings of the National Academy of Sciences 116, 21438–21444 (2019). [8] Kotikian, A., Truby, R. L., Boley, J. W., White, T. J. & Lewis, J. A. 3d printing of liquid crystal elastomeric actuators with spatially programed nematic order. Advanced materials 30, 1706164 (2018). [9] Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kirillova, A. & Ionov, L. Shape-changing polymers for biomedical applications. Journal of Materials Chemistry B 7, 1597–1624 (2019). [6] Viola, J. M. et al. Guiding cell network assembly using shape-morphing hydrogels. Advanced Materials 32, 2002195 (2020). [7] Ford, M. J. et al. A multifunctional shape-morphing elastomer with liquid metal inclusions. Proceedings of the National Academy of Sciences 116, 21438–21444 (2019). [8] Kotikian, A., Truby, R. L., Boley, J. W., White, T. J. & Lewis, J. A. 3d printing of liquid crystal elastomeric actuators with spatially programed nematic order. Advanced materials 30, 1706164 (2018). [9] Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Viola, J. M. et al. Guiding cell network assembly using shape-morphing hydrogels. Advanced Materials 32, 2002195 (2020). [7] Ford, M. J. et al. A multifunctional shape-morphing elastomer with liquid metal inclusions. Proceedings of the National Academy of Sciences 116, 21438–21444 (2019). [8] Kotikian, A., Truby, R. L., Boley, J. W., White, T. J. & Lewis, J. A. 3d printing of liquid crystal elastomeric actuators with spatially programed nematic order. Advanced materials 30, 1706164 (2018). [9] Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ford, M. J. et al. A multifunctional shape-morphing elastomer with liquid metal inclusions. Proceedings of the National Academy of Sciences 116, 21438–21444 (2019). [8] Kotikian, A., Truby, R. L., Boley, J. W., White, T. J. & Lewis, J. A. 3d printing of liquid crystal elastomeric actuators with spatially programed nematic order. Advanced materials 30, 1706164 (2018). [9] Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kotikian, A., Truby, R. L., Boley, J. W., White, T. J. & Lewis, J. A. 3d printing of liquid crystal elastomeric actuators with spatially programed nematic order. Advanced materials 30, 1706164 (2018). [9] Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. 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Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). 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Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. 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IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. 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[52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. 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The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. 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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). 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A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). 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[41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. 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[43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. 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Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kirillova, A. & Ionov, L. Shape-changing polymers for biomedical applications. Journal of Materials Chemistry B 7, 1597–1624 (2019). [6] Viola, J. M. et al. Guiding cell network assembly using shape-morphing hydrogels. Advanced Materials 32, 2002195 (2020). [7] Ford, M. J. et al. A multifunctional shape-morphing elastomer with liquid metal inclusions. Proceedings of the National Academy of Sciences 116, 21438–21444 (2019). [8] Kotikian, A., Truby, R. L., Boley, J. W., White, T. J. & Lewis, J. A. 3d printing of liquid crystal elastomeric actuators with spatially programed nematic order. Advanced materials 30, 1706164 (2018). [9] Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Viola, J. M. et al. Guiding cell network assembly using shape-morphing hydrogels. Advanced Materials 32, 2002195 (2020). [7] Ford, M. J. et al. A multifunctional shape-morphing elastomer with liquid metal inclusions. Proceedings of the National Academy of Sciences 116, 21438–21444 (2019). [8] Kotikian, A., Truby, R. L., Boley, J. W., White, T. J. & Lewis, J. A. 3d printing of liquid crystal elastomeric actuators with spatially programed nematic order. Advanced materials 30, 1706164 (2018). [9] Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ford, M. J. et al. A multifunctional shape-morphing elastomer with liquid metal inclusions. Proceedings of the National Academy of Sciences 116, 21438–21444 (2019). [8] Kotikian, A., Truby, R. L., Boley, J. W., White, T. J. & Lewis, J. A. 3d printing of liquid crystal elastomeric actuators with spatially programed nematic order. Advanced materials 30, 1706164 (2018). [9] Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kotikian, A., Truby, R. L., Boley, J. W., White, T. J. & Lewis, J. A. 3d printing of liquid crystal elastomeric actuators with spatially programed nematic order. Advanced materials 30, 1706164 (2018). [9] Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. 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[52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. 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Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. 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Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). 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Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. 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The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kirillova, A. & Ionov, L. Shape-changing polymers for biomedical applications. Journal of Materials Chemistry B 7, 1597–1624 (2019). [6] Viola, J. M. et al. Guiding cell network assembly using shape-morphing hydrogels. Advanced Materials 32, 2002195 (2020). [7] Ford, M. J. et al. A multifunctional shape-morphing elastomer with liquid metal inclusions. Proceedings of the National Academy of Sciences 116, 21438–21444 (2019). [8] Kotikian, A., Truby, R. L., Boley, J. W., White, T. J. & Lewis, J. A. 3d printing of liquid crystal elastomeric actuators with spatially programed nematic order. Advanced materials 30, 1706164 (2018). [9] Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Viola, J. M. et al. Guiding cell network assembly using shape-morphing hydrogels. Advanced Materials 32, 2002195 (2020). [7] Ford, M. J. et al. A multifunctional shape-morphing elastomer with liquid metal inclusions. Proceedings of the National Academy of Sciences 116, 21438–21444 (2019). [8] Kotikian, A., Truby, R. L., Boley, J. W., White, T. J. & Lewis, J. A. 3d printing of liquid crystal elastomeric actuators with spatially programed nematic order. Advanced materials 30, 1706164 (2018). [9] Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ford, M. J. et al. A multifunctional shape-morphing elastomer with liquid metal inclusions. Proceedings of the National Academy of Sciences 116, 21438–21444 (2019). [8] Kotikian, A., Truby, R. L., Boley, J. W., White, T. J. & Lewis, J. A. 3d printing of liquid crystal elastomeric actuators with spatially programed nematic order. Advanced materials 30, 1706164 (2018). [9] Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kotikian, A., Truby, R. L., Boley, J. W., White, T. J. & Lewis, J. A. 3d printing of liquid crystal elastomeric actuators with spatially programed nematic order. Advanced materials 30, 1706164 (2018). [9] Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). 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[40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. 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A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). 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[54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997).
- Shape-changing polymers for biomedical applications. Journal of Materials Chemistry B 7, 1597–1624 (2019). [6] Viola, J. M. et al. Guiding cell network assembly using shape-morphing hydrogels. Advanced Materials 32, 2002195 (2020). [7] Ford, M. J. et al. A multifunctional shape-morphing elastomer with liquid metal inclusions. Proceedings of the National Academy of Sciences 116, 21438–21444 (2019). [8] Kotikian, A., Truby, R. L., Boley, J. W., White, T. J. & Lewis, J. A. 3d printing of liquid crystal elastomeric actuators with spatially programed nematic order. Advanced materials 30, 1706164 (2018). [9] Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Viola, J. M. et al. Guiding cell network assembly using shape-morphing hydrogels. Advanced Materials 32, 2002195 (2020). [7] Ford, M. J. et al. A multifunctional shape-morphing elastomer with liquid metal inclusions. Proceedings of the National Academy of Sciences 116, 21438–21444 (2019). [8] Kotikian, A., Truby, R. L., Boley, J. W., White, T. J. & Lewis, J. A. 3d printing of liquid crystal elastomeric actuators with spatially programed nematic order. Advanced materials 30, 1706164 (2018). [9] Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ford, M. J. et al. A multifunctional shape-morphing elastomer with liquid metal inclusions. Proceedings of the National Academy of Sciences 116, 21438–21444 (2019). [8] Kotikian, A., Truby, R. L., Boley, J. W., White, T. J. & Lewis, J. A. 3d printing of liquid crystal elastomeric actuators with spatially programed nematic order. Advanced materials 30, 1706164 (2018). [9] Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kotikian, A., Truby, R. L., Boley, J. W., White, T. J. & Lewis, J. A. 3d printing of liquid crystal elastomeric actuators with spatially programed nematic order. Advanced materials 30, 1706164 (2018). [9] Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. 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Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. 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Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). 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Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. 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The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. 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Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. 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Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. 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[44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. 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[8] Kotikian, A., Truby, R. L., Boley, J. W., White, T. J. & Lewis, J. A. 3d printing of liquid crystal elastomeric actuators with spatially programed nematic order. Advanced materials 30, 1706164 (2018). [9] Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kotikian, A., Truby, R. L., Boley, J. W., White, T. J. & Lewis, J. A. 3d printing of liquid crystal elastomeric actuators with spatially programed nematic order. Advanced materials 30, 1706164 (2018). [9] Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. 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Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. 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[25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. 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IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). 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[43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. 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Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. 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[55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. 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Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997).
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Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kotikian, A., Truby, R. L., Boley, J. W., White, T. J. & Lewis, J. A. 3d printing of liquid crystal elastomeric actuators with spatially programed nematic order. Advanced materials 30, 1706164 (2018). [9] Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. 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Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). 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Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. 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A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. 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[35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. 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Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. 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[54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). 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R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Coelho, M., Ishii, H. & Maes, P. in Surflex: a programmable surface for the design of tangible interfaces 3429–3434 (2008). [10] Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. 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[46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. 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[39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. 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[55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. 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[54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997).
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[16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Yu, C. et al. Electronically programmable, reversible shape change in two-and three-dimensional hydrogel structures. Advanced Materials 25, 1541–1546 (2013). [11] Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. 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Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. 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The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. 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[46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. 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R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. 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Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. 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[34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Nojoomi, A., Arslan, H., Lee, K. & Yum, K. Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. 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[44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. 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[20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. 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[25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. 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Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. 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State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). 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[52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997).
- Bioinspired 3d structures with programmable morphologies and motions. Nature communications 9, 1–11 (2018). [12] Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mao, Y. et al. 3d printed reversible shape changing components with stimuli responsive materials. Scientific reports 6, 1–13 (2016). [13] Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wu, J. et al. Multi-shape active composites by 3d printing of digital shape memory polymers. Scientific reports 6, 1–11 (2016). [14] Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. 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[46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. 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[39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). 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[21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). 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[46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Stanley, A. A., Hata, K. & Okamura, A. M. Closed-loop shape control of a haptic jamming deformable surface, 2718–2724 (IEEE, 2016). [15] Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. 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[21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. 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A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. 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R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. 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Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). 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Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. 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Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). 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[40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). 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Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. 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[52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. 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Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. 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R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). 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[46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Duan, H. et al. 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A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Liu, K., Hacker, F. & Daraio, C. Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation. Science Robotics 6, eabf5116 (2021). [16] Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. 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Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. 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Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). 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Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. 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The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. 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Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. 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Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. 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[44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ni, X. et al. Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks. Nature communications 13, 5576 (2022). [17] Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). 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[40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. 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The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). 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[46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zeng, Y. et al. 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R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. 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The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Rauf, A. M., Bernardo, J. S. & Follmer, S. Electroadhesive auxetics as programmable layer jamming skins for formable crust shape displays, 2591–2597 (IEEE, 2023). [18] Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. 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[52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. 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Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). 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[46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. 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The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). 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The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Bai, Y. et al. A dynamically reprogrammable surface with self-evolving shape morphing. Nature 609, 701–708 (2022). [19] Wang, J., Sotzing, M., Lee, M. & Chortos, A. Passively addressed robotic morphing surface (parms) based on machine learning. Science Advances 9, eadg8019 (2023). [20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. 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M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. 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[25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). 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Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. 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R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997).
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[20] Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. 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IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hirota, K. & Hirose, M. Surface display: Concept and implementation approaches, 185–192 (1995). [21] Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. 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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. 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Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. 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[55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. 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Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997).
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[44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. 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Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Iwata, H., Yano, H., Nakaizumi, F. & Kawamura, R. Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. 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Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. 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Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. 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Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. 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A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). 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Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997).
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Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. 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Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. 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Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). 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[46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. 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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. 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Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. 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[55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997).
- Project feelex: adding haptic surface to graphics, 469–476 (2001). [22] Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Leithinger, D. & Ishii, H. Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. 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Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. 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[55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). 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Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. 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Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. 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[54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997).
- Relief: a scalable actuated shape display, 221–222 (2010). [23] Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Follmer, S., Leithinger, D., Olwal, A., Hogge, A. & Ishii, H. inform: dynamic physical affordances and constraints through shape and object actuation., Vol. 13, 2501–988 (2013). [24] Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wang, J., Suo, J. & Chortos, A. Design of fully controllable and continuous programmable surface based on machine learning. IEEE Robotics and Automation Letters 7, 549–556 (2021). [25] Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Hajiesmaili, E., Larson, N. M., Lewis, J. A. & Clarke, D. R. Programmed shape-morphing into complex target shapes using architected dielectric elastomer actuators. Science Advances 8, eabn9198 (2022). [26] Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Chortos, A. et al. Printing reconfigurable bundles of dielectric elastomer fibers. Advanced Functional Materials 2010643 (2021). [27] Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. 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Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. 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The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. 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Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. 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[54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. 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Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xavier, M. S., Fleming, A. J. & Yong, Y. K. 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A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. 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The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. 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Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. 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Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. 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A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. 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A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. 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[34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. 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The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Kotikian, A. et al. Innervated, self-sensing liquid crystal elastomer actuators with closed loop control. Advanced Materials 33, 2101814 (2021). [28] Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. 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Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. 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The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. 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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). 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A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. 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R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. 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Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997).
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A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Jiang, H. et al. A two-level approach for solving the inverse kinematics of an extensible soft arm considering viscoelastic behavior, 6127–6133 (IEEE, 2017). [29] Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Reinhart, R. F. & Steil, J. J. Hybrid mechanical and data-driven modeling improves inverse kinematic control of a soft robot. Procedia Technology 26, 12–19 (2016). [30] Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). 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The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. 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[55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. 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IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. 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Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. 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Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. 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[55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. 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An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). 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Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997).
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[55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3d point clouds: Methods, applications, and its potential use in the construction industry. Automation in Construction 126, 103675 (2021). [31] Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. 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Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Zeng, Y. et al. Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving. IEEE Robotics and Automation Letters 3, 3434–3440 (2018). [32] Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence 43, 4338–4364 (2020). [33] Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Duan, H. et al. Robotics dexterous grasping: The methods based on point cloud and deep learning. Frontiers in Neurorobotics 15, 658280 (2021). [34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). [40] Ma, Z. & Liu, S. A review of 3d reconstruction techniques in civil engineering and their applications. Advanced Engineering Informatics 37, 163–174 (2018). [41] Oehmcke, S. et al. Deep learning based 3d point cloud regression for estimating forest biomass, 1–4 (2022). [42] Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. 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Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). 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[34] Pomerleau, F., Colas, F., Siegwart, R. et al. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1–104 (2015). [35] Zhang, J., Zhao, X., Chen, Z. & Lu, Z. A review of deep learning-based semantic segmentation for point cloud. IEEE access 7, 179118–179133 (2019). [36] Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. State of the art in surface reconstruction from point clouds, CONF (The Eurographics Association, 2014). 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Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. 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Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). 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R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. 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[54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Lv, K. et al. Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. 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Learning to estimate 3-d states of deformable linear objects from single-frame occluded point clouds, 7119–7125 (IEEE, 2023). [43] Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. 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The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xie, Y., Tian, J. & Zhu, X. X. Linking points with labels in 3d: A review of point cloud semantic segmentation. IEEE Geoscience and remote sensing magazine 8, 38–59 (2020). [37] Grilli, E., Menna, F. & Remondino, F. A review of point clouds segmentation and classification algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42, 339–344 (2017). [38] Zhang, H. et al. Deep learning-based 3d point cloud classification: A systematic survey and outlook. Displays 102456 (2023). [39] Berger, M. et al. 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R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017). [48] Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. 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[54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). 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Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Chen, X., Wang, G., Zhang, C., Kim, T.-K. & Ji, X. Shpr-net: Deep semantic hand pose regression from point clouds. IEEE Access 6, 43425–43439 (2018). [44] Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. 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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Ge, L., Liang, H., Yuan, J. & Thalmann, D. 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images, 1991–2000 (2017). [45] Li, Y. et al. Pointcnn: Convolution on x-transformed points. Advances in neural information processing systems 31 (2018). [46] Qi, C. R., Su, H., Mo, K. & Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation, 652–660 (2017). [47] Qi, C. R., Yi, L., Su, H. & Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. 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IEEE Transactions on Image Processing 6, 1305–1315 (1997). Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997).
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The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wu, T. et al. 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IEEE Transactions on Image Processing 6, 1305–1315 (1997). Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997).
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Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Thomas, H. et al. Kpconv: Flexible and deformable convolution for point clouds, 6411–6420 (2019). [49] Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Liu, Y., Fan, B., Xiang, S. & Pan, C. Relation-shape convolutional neural network for point cloud analysis, 8895–8904 (2019). [50] Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mann, A., Bürgel, C. M. & Groche, P. A modeling strategy for predicting the properties of paraffin wax actuators, Vol. 7, 81 (MDPI, 2018). [51] Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Wu, T. et al. Balanced chamfer distance as a comprehensive metric for point cloud completion. Advances in Neural Information Processing Systems 34, 29088–29100 (2021). [52] Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Mémoli, F. & Sapiro, G. Comparing point clouds, 32–40 (2004). [53] Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Xavier, M. S., Fleming, A. J. & Yong, Y. K. Finite element modeling of soft fluidic actuators: Overview and recent developments. Advanced Intelligent Systems 3, 2000187 (2021). [54] Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics, Vol. 7, 48 (MDPI, 2018). [55] Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997). Eldar, Y., Lindenbaum, M., Porat, M. & Zeevi, Y. Y. The farthest point strategy for progressive image sampling. IEEE Transactions on Image Processing 6, 1305–1315 (1997).
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