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FF-LOGO: Cross-Modality Point Cloud Registration with Feature Filtering and Local to Global Optimization (2309.08966v2)

Published 16 Sep 2023 in cs.CV

Abstract: Cross-modality point cloud registration is confronted with significant challenges due to inherent differences in modalities between different sensors. We propose a cross-modality point cloud registration framework FF-LOGO: a cross-modality point cloud registration method with feature filtering and local-global optimization. The cross-modality feature correlation filtering module extracts geometric transformation-invariant features from cross-modality point clouds and achieves point selection by feature matching. We also introduce a cross-modality optimization process, including a local adaptive key region aggregation module and a global modality consistency fusion optimization module. Experimental results demonstrate that our two-stage optimization significantly improves the registration accuracy of the feature association and selection module. Our method achieves a substantial increase in recall rate compared to the current state-of-the-art methods on the 3DCSR dataset, improving from 40.59% to 75.74%. Our code will be available at https://github.com/wangmohan17/FFLOGO.

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References (29)
  1. K. Li, J. Yang, Y.-K. Lai, and D. Guo, “Robust non-rigid registration with reweighted position and transformation sparsity,” IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 6, pp. 2255–2269, 2018.
  2. W. Lu, G. Wan, Y. Zhou, X. Fu, P. Yuan, and S. Song, “Deepvcp: An end-to-end deep neural network for point cloud registration,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 12–21.
  3. V. Ferrari, N. Cattari, U. Fontana, and F. Cutolo, “Parallax free registration for augmented reality optical see-through displays in the peripersonal space,” IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 3, pp. 1608–1618, 2020.
  4. D. Cattaneo, M. Vaghi, and A. Valada, “Lcdnet: Deep loop closure detection and point cloud registration for lidar slam,” IEEE Transactions on Robotics, vol. 38, no. 4, pp. 2074–2093, 2022.
  5. F. Pomerleau, F. Colas, R. Siegwart, et al., “A review of point cloud registration algorithms for mobile robotics,” Foundations and Trends® in Robotics, vol. 4, no. 1, pp. 1–104, 2015.
  6. P. J. Besl and N. D. McKay, “Method for registration of 3-d shapes,” in Sensor fusion IV: control paradigms and data structures, vol. 1611.   Spie, 1992, pp. 586–606.
  7. D. Chetverikov, D. Stepanov, and P. Krsek, “Robust euclidean alignment of 3d point sets: the trimmed iterative closest point algorithm,” Image and Vision Computing, vol. 23, no. 3, pp. 299–309, 2005.
  8. M. L. Tazir, T. Gokhool, P. Checchin, L. Malaterre, and L. Trassoudaine, “Cicp: Cluster iterative closest point for sparse–dense point cloud registration,” Robotics and Autonomous Systems, vol. 108, pp. 66–86, 2018.
  9. D. Aiger, N. J. Mitra, and D. Cohen-Or, “4-points congruent sets for robust pairwise surface registration,” in ACM SIGGRAPH 2008 papers, 2008, pp. 1–10.
  10. N. Mellado, D. Aiger, and N. J. Mitra, “Super 4pcs fast global pointcloud registration via smart indexing,” vol. 33, no. 5, pp. 205–215, 2014.
  11. B. Jian and B. C. Vemuri, “Robust point set registration using gaussian mixture models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 8, pp. 1633–1645, 2010.
  12. W. Gao and R. Tedrake, “Filterreg: Robust and efficient probabilistic point-set registration using gaussian filter and twist parameterization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 11 095–11 104.
  13. X. Huang, J. Zhang, L. Fan, Q. Wu, and C. Yuan, “A systematic approach for cross-source point cloud registration by preserving macro and micro structures,” IEEE Transactions on Image Processing, vol. 26, no. 7, pp. 3261–3276, 2017.
  14. X. Huang, L. Fan, Q. Wu, J. Zhang, and C. Yuan, “Fast registration for cross-source point clouds by using weak regional affinity and pixel-wise refinement,” pp. 1552–1557, 2019.
  15. M. Zhao, X. Huang, J. Jiang, L. Mou, D.-M. Yan, and L. Ma, “Accurate registration of cross-modality geometry via consistent clustering,” IEEE Transactions on Visualization and Computer Graphics, 2023.
  16. X. Huang, G. Mei, J. Zhang, and R. Abbas, “A comprehensive survey on point cloud registration,” arXiv preprint arXiv:2103.02690, 2021.
  17. C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 652–660.
  18. Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon, “Dynamic graph cnn for learning on point clouds,” ACM Transactions on Graphics, vol. 38, no. 5, pp. 1–12, 2019.
  19. S. Ao, Q. Hu, B. Yang, A. Markham, and Y. Guo, “Spinnet: Learning a general surface descriptor for 3d point cloud registration,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 11 753–11 762.
  20. X. Bai, Z. Luo, L. Zhou, H. Fu, L. Quan, and C.-L. Tai, “D3feat: Joint learning of dense detection and description of 3d local features,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 6359–6367.
  21. C. Choy, W. Dong, and V. Koltun, “Deep global registration,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 2514–2523.
  22. X. Huang, G. Mei, and J. Zhang, “Feature-metric registration: A fast semi-supervised approach for robust point cloud registration without correspondences,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 11 366–11 374.
  23. Z. Qin, H. Yu, C. Wang, Y. Guo, Y. Peng, and K. Xu, “Geometric transformer for fast and robust point cloud registration,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 11 143–11 152.
  24. S. Peng, Y. Liu, Q. Huang, X. Zhou, and H. Bao, “Pvnet: Pixel-wise voting network for 6dof pose estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 4561–4570.
  25. D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014.
  26. A. Zeng, S. Song, M. Nießner, M. Fisher, J. Xiao, and T. Funkhouser, “3dmatch: Learning local geometric descriptors from rgb-d reconstructions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 1802–1811.
  27. M. A. Fischler and R. C. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Communications of the ACM, vol. 24, no. 6, pp. 381–395, 1981.
  28. I. Jubran, A. Maalouf, R. Kimmel, and D. Feldman, “Provably approximated point cloud registration,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 13 269–13 278.
  29. J. Li, C. Zhang, Z. Xu, H. Zhou, and C. Zhang, “Iterative distance-aware similarity matrix convolution with mutual-supervised point elimination for efficient point cloud registration,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIV 16.   Springer, 2020, pp. 378–394.
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