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This work studies the problem of unsupervised RGB-D point cloud registration, which aims at training a robust registration model without ground-truth pose supervision.
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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
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2022
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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
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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
2020
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2020
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2021
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2021
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2022
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2023
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M. Yuan, K. Fu, Z. Li, Y. Meng, and M. Wang, “Pointmbf: A multi-scale bidirectional fusion network for unsupervised rgb-d point cloud registration,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 17 694–17 705
2023
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H. Wang, J. Wang, and L. Agapito, “Co-slam: Joint coordinate and sparse parametric encodings for neural real-time slam,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 13 293–13 302
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2024
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