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Point cloud registration involves determining a rigid transformation to align a source point cloud with a target point cloud.
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M. A. Uy, Q.-H. Pham, B.-S. Hua, T. Nguyen, and S.-K. Yeung, “Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data,” in Proc. IEEE Int. Conf. Comput. Vis. , 2019, pp. 1588–1597
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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 Proc. IEEE Int. Conf. Comput. Vis. , 2019, pp. 12–21
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2024
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K. Slimani, C. Achard, and B. Tamadazte, “Rocnet++: Triangle-based descriptor for accurate and robust point cloud registration,” Pattern Recognit. , vol. 147, p. 110108, 2024
2024
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B. Zhao, Q. Liu, Z. Wang, X. Chen, Z. Jia, and D. Liang, “Ha-tinet: Learning a distinctive and general 3d local descriptor for point cloud registration,” IEEE Trans. Vis. Comput. Graph. , 2024
2024
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Y. Liu and Z. Liu, “Low overlapping point cloud registration using mutual prior based completion network,” IEEE Trans. Image Process. , vol. 33, pp. 4781–4795, 2024
2024
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M. Yuan, K. Fu, Z. Li, Y. Meng, A. Shen, and M. Wang, “Robust point cloud registration via random network co-ensemble,” IEEE Trans. Circuits Syst. Video Technol. , 2024
2024
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T. Huang, L. Peng, R. Vidal, and Y.-H. Liu, “Scalable 3d registration via truncated entry-wise absolute residuals,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2024, pp. 27 477–27 487
2024
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J. Yang, X. Zhang, P. Wang, Y. Guo, K. Sun, Q. Wu, S. Zhang, and Y. Zhang, “Mac: Maximal cliques for 3d registration,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 46, no. 12, pp. 10 645–10 662, 2024
2024
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Y. Zhang, H. Zhao, H. Li, and S. Chen, “Fastmac: Stochastic spectral sampling of correspondence graph,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2024, pp. 17 857–17 867
2024
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Y. Yuan, Y. Wu, J. Lei, C. Hu, M. Gong, X. Fan, W. Ma, and Q. Miao, “Learning compact transformation based on dual quaternion for point cloud registration,” IEEE Trans. Instrum. Meas. , vol. 73, 2024
2024
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S. Jin, D. Barath, M. Pollefeys, and I. Armeni, “Q-reg: End-to-end trainable point cloud registration with surface curvature,” in Proc. Int. Conf.3D Vis. IEEE, 2024, pp. 1330–1339
2024
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R. She, S. Wang, Q. Kang, K. Zhao, Y. Song, W. P. Tay, T. Geng, and X. Jian, “Posdiffnet: Positional neural diffusion for point cloud registration in a large field of view with perturbations,” in Proc. AAAI Conf. Artif. Intell. , vol. 38, no. 1, 2024, pp. 231–239
2024
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R. She, Q. Kang, S. Wang, W. P. Tay, K. Zhao, Y. Song, T. Geng, Y. Xu, D. N. Navarro, and A. Hartmannsgruber, “Pointdifformer: Robust point cloud registration with neural diffusion and transformer,” IEEE Trans. Geosci. Remote Sens. , 2024
2024
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H. Jiang, M. Salzmann, Z. Dang, J. Xie, and J. Yang, “Se (3) diffusion model-based point cloud registration for robust 6d object pose estimation,” Proc. Int. Conf. Neural Inf. Process. Syst. , vol. 36, 2024
2024
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Z. Xu, X. Jiang, X. Gao, R. Gao, C. Gu, Q. Zhang, W. Li, and X. Gao, “Igreg: Image-geometry-assisted point cloud registration via selective correlation fusion,” IEEE Trans. Multimed. , vol. 26, pp. 7475–7489, 2024
2024
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S. Chen, H. Xu, H. Li, K. Luo, G. Liu, C.-W. Fu, P. Tan, and S. Liu, “Pointreggpt: Boosting 3d point cloud registration using generative point-cloud pairs for training,” Proc. Eur. Conf. Comput. Vis. , 2024
2024
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K. Xiong, M. Zheng, Q. Xu, C. Wen, S. Shen, and C. Wang, “Speal: Skeletal prior embedded attention learning for cross-source point cloud registration,” in Proc. AAAI Conf. Artif. Intell. , vol. 38, no. 6, 2024, pp. 6279–6287
2024
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Y. Wang, P. Zhou, G. Geng, L. An, K. Li, and R. Li, “Neighborhood multi-compound transformer for point cloud registration,” IEEE Trans. Circuits Syst. Video Technol. , vol. 34, no. 9, pp. 8469–8480, 2024
2024
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H. Chen, P. Yan, S. Xiang, and Y. Tan, “Dynamic cues-assisted transformer for robust point cloud registration,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2024, pp. 21 698–21 707
2024
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Z. Yu, Z. Qin, L. Zheng, and K. Xu, “Learning instance-aware correspondences for robust multi-instance point cloud registration in cluttered scenes,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2024, pp. 19 605–19 614
2024
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2024
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R. Li, X. Yuan, S. Gan, R. Bi, S. Gao, W. Luo, and C. Chen, “An effective point cloud registration method based on robust removal of outliers,” IEEE Trans. Geosci. Remote Sens. , vol. 62, 2024
2024
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J. Han, M. Shin, and J. Paik, “Robust point cloud registration using hough voting-based correspondence outlier rejection,” Eng. Appl. Artif. Intell. , vol. 133, p. 107985, 2024
2024
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G. Ma, H. Wei, R. Lin, and J. Wu, “Pcgor: A novel plane constraints-based guaranteed outlier removal method for large-scale lidar point cloud registration,” IEEE Trans. Geosci. Remote Sens. , 2024
2024
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S. Li, J. Zhu, and Y. Xie, “Dbdnet: Partial-to-partial point cloud registration with dual branches decoupling,” Knowl.-Based Syst. , vol. 296, p. 111864, 2024
2024
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2024
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X. Zheng, X. Huang, G. Mei, Y. Hou, Z. Lyu, B. Dai, W. Ouyang, and Y. Gong, “Point cloud pre-training with diffusion models,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2024, pp. 22 935–22 945
2024
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H. Yu, J. Hou, Z. Qin, M. Saleh, I. Shugurov, K. Wang, B. Busam, and S. Ilic, “Riga: Rotation-invariant and globally-aware descriptors for point cloud registration,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 46, no. 5, pp. 3796–3812, 2024
2024
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2024
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Z. Zhao, J. Kang, L. Feng, J. Liang, Y. Ren, and B. Wu, “Lfa-net: Enhanced pointnet and assignable weights transformer network for partial-to-partial point cloud registration,” IEEE Trans. Circuits Syst. Video Technol. , 2024
2024
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Y. Yuan, Y. Wu, M. Yue, M. Gong, X. Fan, W. Ma, and Q. Miao, “Learning discriminative features via multi-hierarchical mutual information for unsupervised point cloud registration,” IEEE Trans. Circuits Syst. Video Technol. , 2024
2024
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C. Yan, M. Feng, Z. Wu, Y. Guo, W. Dong, Y. Wang, and A. Mian, “Discriminative correspondence estimation for unsupervised rgb-d point cloud registration,” IEEE Trans. Circuits Syst. Video Technol. , 2024
2024
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2024
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Y. Jiang, B. Zhou, X. Liu, Q. Li, and C. Cheng, “Gtinet: Global topology-aware interactions for unsupervised point cloud registration,” IEEE Trans. Circuits Syst. Video Technol. , 2024
2024
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Y. Yuan, Y. Wu, X. Fan, M. Gong, Q. Miao, and W. Ma, “Inlier confidence calibration for point cloud registration,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2024, pp. 5312–5321
2024
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C. Zheng, M. Ma, Z. Chen, H. Chen, W. Wang, and M. Wei, “Regiformer: Unsupervised point cloud registration via geometric local-to-global transformer and self augmentation,” IEEE Trans. Geosci. Remote Sens. , vol. 62, 2024
2024
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Q. Liu, H. Zhu, Z. Wang, Y. Zhou, S. Chang, and M. Guo, “Extend your own correspondences: Unsupervised distant point cloud registration by progressive distance extension,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2024, pp. 20 816–20 826
2024
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K. Xiong, H. Xiang, Q. Xu, C. Wen, S. Shen, J. Li, and C. Wang, “Mining and transferring feature-geometry coherence for unsupervised point cloud registration,” Proc. Int. Conf. Neural Inf. Process. Syst. , 2024
2024
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2024
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R. Yao, S. Du, W. Cui, C. Tang, and C. Yang, “Pare-net: Position-aware rotation-equivariant networks for robust point cloud registration,” in Proc. Eur. Conf. Comput. Vis. Springer, 2025, pp. 287–303
2025
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Q. Wu, H. Jiang, L. Luo, J. Li, Y. Ding, J. Xie, and J. Yang, “Diff-reg: diffusion model in doubly stochastic matrix space for registration problem.” Springer, 2025, pp. 160–178
2025
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S. Fung, X. Lu, D. d. S. Edirimuni, W. Pan, X. Liu, and H. Li, “Semreg: Semantics constrained point cloud registration,” in Proc. Eur. Conf. Comput. Vis. Springer, 2025, pp. 293–310
2025
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Y. Haitman, A. Efraim, and J. M. Francos, “Uneregrobust-universal manifold embedding compatible features for robust point cloud registration,” in Proc. Eur. Conf. Comput. Vis. Springer, 2025, pp. 358–374
2025
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