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Point cloud registration is a crucial problem in computer vision and robotics.
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2017
Cited alongside, same era.
P. Kim, J. Chen, and Y. K. Cho, “Slam-driven robotic mapping and registration of 3d point clouds,” Automation in Construction , vol. 89, pp. 38–48, 2018
2018
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H. Deng, T. Birdal, and S. Ilic, “Ppfnet: Global context aware local features for robust 3d point matching,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 195–205
2018
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C. Esteves, C. Allen-Blanchette, A. Makadia, and K. Daniilidis, “Learning so (3) equivariant representations with spherical cnns,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 52–68
2018
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F. Fuchs, D. Worrall, V. Fischer, and M. Welling, “Se (3)-transformers: 3d roto-translation equivariant attention networks,” Advances in Neural Information Processing Systems , vol. 33, pp. 1970–1981, 2020
2020
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Z. J. Yew and G. H. Lee, “Rpm-net: Robust point matching using learned features,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 824–11 833
2020
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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
2020
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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
2020
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R. Kondor and S. Trivedi, “On the generalization of equivariance and convolution in neural networks to the action of compact groups,” in International Conference on Machine Learning . PMLR, 2018, pp. 2747–2755
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Y. Wang and J. M. Solomon, “Deep closest point: Learning representations for point cloud registration,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 3523–3532
2019
Cited alongside, same era.
Z. Gojcic, C. Zhou, J. D. Wegner, and A. Wieser, “The perfect match: 3d point cloud matching with smoothed densities,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 5545–5554
2019
Cited alongside, same era.
C. Choy, J. Park, and V. Koltun, “Fully convolutional geometric features,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 8958–8966
2019
Cited alongside, same era.
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
2019
Cited alongside, same era.
Y. Wang and J. M. Solomon, “Prnet: Self-supervised learning for partial-to-partial registration,” Advances in neural information processing systems , vol. 32, 2019
2019
Cited alongside, same era.
D. Campbell, L. Petersson, L. Kneip, H. Li, and S. Gould, “The alignment of the spheres: Globally-optimal spherical mixture alignment for camera pose estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 11 796–11 806
2019
Cited alongside, same era.
A. Drory, T. Shomer, S. Avidan, and R. Giryes, “Best buddies registration for point clouds,” in Proceedings of the Asian Conference on Computer Vision , 2020
2020
Later among the works it cites.
D. Bauer, T. Patten, and M. Vincze, “Reagent: Point cloud registration using imitation and reinforcement learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 14 586–14 594
2021
Later among the works it cites.
C. Deng, O. Litany, Y. Duan, A. Poulenard, A. Tagliasacchi, and L. J. Guibas, “Vector neurons: A general framework for so (3)-equivariant networks,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 12 200–12 209
2021
Later among the works it cites.
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
2021
Later among the works it cites.
S. Huang, Z. Gojcic, M. Usvyatsov, A. Wieser, and K. Schindler, “Predator: Registration of 3d point clouds with low overlap,” in Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition , 2021, pp. 4267–4276
2021
Later among the works it cites.
2021
Later among the works it cites.
D. Cattaneo, M. Vaghi, and A. Valada, “Lcdnet: Deep loop closure detection and point cloud registration for lidar slam,” IEEE Transactions on Robotics , 2022
2022
Closest in time.
M. Zhu, M. Ghaffari, and H. Peng, “Correspondence-free point cloud registration with so(3)-equivariant implicit shape representations,” in Conference on Robot Learning . PMLR, 2022, pp. 1412–1422
2022
Closest in time.
H. Chen, Z. Wei, Y. Xu, M. Wei, and J. Wang, “Imlovenet: Misaligned image-supported registration network for low-overlap point cloud pairs,” in ACM SIGGRAPH 2022 Conference Proceedings , 2022, pp. 1–9
2022
Closest in time.
Z. Chen, F. Yang, and W. Tao, “Detarnet: Decoupling translation and rotation by siamese network for point cloud registration,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 1, 2022, pp. 401–409
2022
Closest in time.
Z. J. Yew and G. H. Lee, “Regtr: End-to-end point cloud correspondences with transformers,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 6677–6686
2022
Closest in time.
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
2022
Closest in time.