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All issues Volume 21 / No 2 (2025) J. Eur. Opt. Society-Rapid Publ., 21 2 (2025) 40 References
Open Access
Issue
J. Eur. Opt. Society-Rapid Publ.
Volume 21, Number 2, 2025
Article Number 40
Number of page(s) 11
DOI https://doi.org/10.1051/jeos/2025035
Published online 12 September 2025
  1. Servos J, Waslander SL, Multi channel generalized - ICP, in Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA) (Hong Kong, China, 2014), pp. 3644–3649. https://doi.org/10.1109/ICRA.2014.6907386. [Google Scholar]
  2. Yang J, Li H, Campbell D, Jia Y, Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration, IEEE Trans. Pattern Anal. Mach. Intell. 38(11), 2241–2254 (2016). https://doi.org/10.1109/TPAMI.2015.2513405. [Google Scholar]
  3. Koide K, Yokozuka M, Oishi S, Banno A, Voxelized GICP for fast and accurate 3D point cloud registration, in: Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA) (Xi’an, China, 2021), pp. 11054–11059. https://doi.org/10.1109/ICRA48506.2021.9560835. [Google Scholar]
  4. Biber P, Strasser W, The normal distributions transform: a new approach to laser scan matching, in: Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (NV, USA, 2003), pp. 2743–2748. https://doi.org/10.1109/IROS.2003.1249285. [Google Scholar]
  5. Magnusson M, The three-dimensional normal-distributions transform : an efficient representation for registration, surface analysis, and loop detection, in: Ph.D., Örebro University, 2009. [Google Scholar]
  6. Rusu RB, Blodow N, Beetz M, Fast Point Feature Histograms (FPFH) for 3D registration, in: Proceedings of the 2009 IEEE International Conference on Robotics and Automation (Kobe, Japan, 2009), pp. 3212–3217. https://doi.org/10.1109/ROBOT.2009.5152473. [Google Scholar]
  7. Salti S, Tombari F, Stefano LD, SHOT: Unique signatures of histograms for surface and texture description, Comput. Vis. Image. Und. 125, 251–264 (2015). https://doi.org/10.1016/j.cviu.2014.04.011. [Google Scholar]
  8. Rusu RB, Bradski G, Thibaux R, Hsu J, Fast 3D recognition and pose using the Viewpoint Feature Histogram, in: Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (Taipei, Taiwan, 2010), pp. 2155–2162. https://doi.org/10.1109/IROS.2010.5651280. [Google Scholar]
  9. Aoki Y, Goforth H, Srivatsan RA, Lucey S, PointNetLK: Robust and Efficient Point Cloud Registration Using PointNet, in: Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (CA, USA, 2019), pp. 7156–7165. https://doi.org/10.1109/CVPR.2019.00733. [Google Scholar]
  10. Lu W, Wan G, Zhou Y, Fu X, Yuan P, Song S, DeepVCP: An end-to-end deep neural network for point cloud registration, in: Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (Seoul, Korea (South), 2019), 12–21. https://doi.org/10.1109/ICCV.2019.00010. [Google Scholar]
  11. Wang Y, Solomon J, Deep closest point: learning representations for point cloud registration, in: Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (Seoul, Korea (South), 2019), 3522–3531. https://doi.org/10.1109/ICCV.2019.00362. [Google Scholar]
  12. Wang Y, Solomon J, PRNet: Self-Supervised Learning for Partial-to-Partial Registration, in: Proceedings of Neural Information Processing Systems (NIPS) (NY, USA, 2019), pp. 8814–8826. https://dl.acm.org/doi/abs/10.5555/3295222.3295263. [Google Scholar]
  13. Li J, Zhang C, Xu Z, Zhou H, Zhang C, Iterative distance-aware similarity matrix convolution with mutual-supervised point elimination for efficient point cloud registration, in: Proceedings of 16th European Conference on Computer Vision (ECCV2020) (Glasgow, UK, 2020), pp. 378–394. https://doi.org/10.1007/978-3-030-58586-0_23. [Google Scholar]
  14. Yew ZJ, Lee GH, RPM-Net: Robust Point Matching Using Learned Features, in Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (WA, USA, 2020), pp. 11821–11830. https://doi.org/10.1109/CVPR42600.2020.01184. [Google Scholar]
  15. Pais GD, Ramalingam S, Govindu VM, Nascimento JC, Chellappa R, Miraldo P, 3DRegNet: A Deep Neural Network for 3D Point Registration, in: Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (WA, USA, 2020), pp. 7191–7201. https://doi.org/10.1109/CVPR42600.2020.00722. [Google Scholar]
  16. Yang J, Zhang X, Wang P, Guo Y, Sun K, Wu Q, Zhang S, Zhang Y, MAC: Maximal Cliques for 3D Registration, IEEE Trans. Pattern Anal. Mach. Intell. 46(12), 10645–10662 (2024). https://doi.org/10.1109/TPAMI.2024.3442911. [Google Scholar]
  17. Huang S, Gojcic Z, Usvyatsov M, Wieser M, Schindler K, PREDATOR: Registration of 3D Point Clouds with Low Overlap, in: Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA (2021), pp. 4265–4274. https://doi.org/10.1109/CVPR46437.2021.00425. [Google Scholar]
  18. Ao S, Hu Q, Wang H, Xu K, Guo Y, BUFFER: Balancing Accuracy, Efficiency, and Generalizability in Point Cloud Registration, in: Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (BC, Canada, 2023), pp. 1255–1264. https://doi.org/10.1109/CVPR52729.2023.00127. [Google Scholar]
  19. Charles RQ, Su H, Kaichun M, Guibas LJ, PointNet: Deep learning on point sets for 3D classification and segmentation, in: Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (HI, USA, 2017), pp. 77–85. https://doi.org/10.1109/CVPR.2017.16. [Google Scholar]
  20. Charles RQ, Yi L, Hao S, Leonidas JG, PointNet++: deep hierarchical feature learning on point sets in a metric space, in: Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS’17) (NY, USA, 2017), pp. 5105–5114. https://dl.acm.org/doi/abs/10.5555/3295222.3295263. [Google Scholar]
  21. Wang Y, Sun Y, Liu Z, Sarma S, Bronstein M, Solomon J, Dynamic graph CNN for learning on point clouds, ACM Trans. Graph. 38(5), 1–12 (2019). https://doi.org/10.1145/3326362. [Google Scholar]
  22. Xia T, Lin J, Li Y, Feng J, Hui P, Sun F, Guo D, Jin D, 3DGCN: 3-Dimensional Dynamic Graph Convolutional Network for Citywide Crowd Flow Prediction, ACM Trans. Knowl. Discov. Data. 15(6), 1–21 (2021). https://doi.org/10.1145/3451394. [Google Scholar]
  23. Xu G, Pang Y, Bai Z, Wang Y, Lu Z, A fast point clouds registration algorithm for laser scanners, Appl. Sci. 11, 3426 (2021). https://doi.org/10.3390/app11083426. [Google Scholar]
  24. Gojcic Z, Zhou C, Wegner JD, Wieser A, The perfect match: 3d point cloud matching with smoothed densities, in Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (CA, USA, 2019), pp. 5540–5549. https://doi.org/10.1109/CVPR.2019.00569. [Google Scholar]
  25. Choy C, Park J, Koltun V, Fully Convolutional Geometric Features, in: Proceedings of 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (Seoul, Korea (South), 2019), pp. 8957–8965. https://doi.org/10.1109/ICCV.2019.00905. [Google Scholar]
  26. Bai X, Luo Z, Zhou L, Fu H, Quan L, Tai C-L, D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features, in: Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (WA, USA, 2020), pp. 6358–6366. https://doi.org/10.1109/CVPR42600.2020.00639. [Google Scholar]
  27. Ao S, Hu Q, Yang B, Markham A, Guo Y, SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration, in: Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (TN, USA, 2021), pp. 11748–11757. https://doi.org/10.1109/CVPR46437.2021.01158. [Google Scholar]
  28. Wang H, Liu Y, Dong Z, Wang W, You only hypothesize once: point cloud registration with rotation-equivariant descriptors, in: Proceedings of 30th ACM International Conference on Multimedia (MM ‘22) (NY, USA, 2022), pp. 1630–1641. https://doi.org/10.1145/3503161.3548023. [Google Scholar]
  29. Yu H, Li F, Saleh M, Busam B, Ilic S, CoFiNet: reliable coarse-to-fine correspondences for robust point cloud registration, in: Proceedings of 35th International Conference on Neural Information Processing Systems (NIPS ‘21) (NY, USA, 2021), pp. 23872-23884. https://dl.acm.org/doi/10.5555/3540261.3542089. [Google Scholar]
  30. Yew ZJ, Lee GH, 3Dfeat-net: Weakly supervised local 3d features for point cloud registration, in: Proceedings of 15th European Conference on Computer Vision (ECCV 2018) (Munich, Germany, 2018), pp. 630–646. https://doi.org/10.1007/978-3-030-01267-0_37. [Google Scholar]

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