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邱巧燕 等: 局部-全局动态图学习与互补融合的点云配准方法 5753

[14] Xu H, Liu SC, Wang GF, Liu GH, Zeng B. OMNet: Learning overlapping mask for partial-to-partial point cloud registration. In: Proc. of
the 2021 IEEE/CVF Int’l Conf. on Computer Vision. Montreal: IEEE, 2021. 3112–3121. [doi: 10.1109/ICCV48922.2021.00312]
[15] Lu WX, Wan GW, Zhou Y, Fu XY, Yuan PF, Song SY. DeepVCP: An end-to-end deep neural network for point cloud registration. In:
Proc. of the 2019 IEEE/CVF Int’l Conf. on Computer Vision. Seoul: IEEE, 2019. 12–21. [doi: 10.1109/ICCV.2019.00010]
[16] Qi CR, Yi L, Su H, Guibas LJ. PointNet++: Deep hierarchical feature learning on point sets in a metric space. In: Proc. of the 31st Int’l
Conf. on Neural Information Processing Systems. Long Beach: Curran Associates Inc., 2017. 5105–5114.
[17] Yew ZJ, Lee GH. RPM-Net: Robust point matching using learned features. In: Proc. of the 2020 IEEE/CVF Conf. on Computer Vision
and Pattern Recognition. Seattle: IEEE, 2020. 11821–11830. [doi: 10.1109/CVPR42600.2020.01184]
[18] Li JH, Zhang CH, Xu ZY, Zhou HN, Zhang C. Iterative distance-aware similarity matrix convolution with mutual-supervised point
elimination for efficient point cloud registration. In: Proc. of the 16th European Conf. on Computer Vision. Glasgow: Springer, 2020.
378–394. [doi: 10.1007/978-3-030-58586-0_23]
[19] Mei Q, Wang FX, Tong C, Zhang JQ, Jiang B, Xiao J. PACNet: A high-precision point cloud registration network based on deep
learning. In: Proc. of the 13th Int’l Conf. on Wireless Communications and Signal Processing. Changsha: IEEE, 2021. 1–5. [doi: 10.1109/
WCSP52459.2021.9613579]
[20] Qin Z, Yu H, Wang CJ, Guo YL, Peng YX, Ilic S, Hu DW, Xu K. GeoTransformer: Fast and robust point cloud registration with
geometric Transformer. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2023, 45(8): 9806–9821. [doi: 10.1109/TPAMI.2023.
3259038]
[21] Slimani K, Achard C, Tamadazte B. RoCNet++: Triangle-based descriptor for accurate and robust point cloud registration. Pattern
Recognition, 2024, 147: 110108. [doi: 10.1016/j.patcog.2023.110108]
[22] Xie YF, Wang BY, Li SQ, Zhu JH. Iterative feedback network for unsupervised point cloud registration. IEEE Robotics and Automation
Letters, 2024, 9(3): 2327–2334. [doi: 10.1109/LRA.2024.3355784]
[23] Wang Y, Solomon J. Deep closest point: Learning representations for point cloud registration. In: Proc. of the 2019 IEEE/CVF Int’l Conf.
on Computer Vision. Seoul: IEEE, 2019. 3522–3531. [doi: 10.1109/ICCV.2019.00362]
[24] Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. Attention is all you need. In: Proc. of the
31st Int’l Conf. on Neural Information Processing Systems. Long Beach: Curran Associates Inc., 2017. 6000–6010.
[25] Huang SY, Gojcic Z, Usvyatsov M, Wieser A, Schindler K. PREDATOR: Registration of 3D point clouds with low overlap. In: Proc. of
the 2021 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021. 4265–4274. [doi: 10.1109/CVPR46437.
2021.00425]
[26] Fischer K, Simon M, Ölsner F, Milz S, Groβ HM, Mäder P. StickyPillars: Robust and efficient feature matching on point clouds using
graph neural networks. In: Proc. of the 2021 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021.
313–323. [doi: 10.1109/CVPR46437.2021.00038]
[27] Shi CH, Chen XYL, Huang KH, Xiao JH, Lu HM, Stachniss C. Keypoint matching for point cloud registration using multiplex dynamic
graph attention networks. IEEE Robotics and Automation Letters, 2021, 6(4): 8221–8228. [doi: 10.1109/LRA.2021.3097275]
[28] Kadam P, Zhang M, Liu S, Kuo CCJ. R-PointHop: A green, accurate, and unsupervised point cloud registration method. IEEE Trans. on
Image Processing, 2022, 31: 2710–2725. [doi: 10.1109/TIP.2022.3160609]
[29] Chen GY, Wang ML, Zhang QX, Yuan L, Yue YF. Full Transformer framework for robust point cloud registration with deep information
interaction. IEEE Trans. on Neural Networks and Learning Systems, 2024, 35(10): 13368–13382. [doi: 10.1109/TNNLS.2023.3267333]
[30] Jiang YN, Zhou BT, Liu XY, Li QY, Cheng C. GTINet: Global topology-aware interactions for unsupervised point cloud registration.
IEEE Trans. on Circuits and Systems for Video Technology, 2024, 34(7): 6363–6375. [doi: 10.1109/TCSVT.2024.3367529]
[31] Wu BL, Ma J, Chen GJ, An P. Feature interactive representation for point cloud registration. In: Proc. of the 2021 IEEE/CVF Int’l Conf.
on Computer Vision. Montreal: IEEE, 2021. 5510–5519. [doi: 10.1109/ICCV48922.2021.00548]
[32] Xu H, Ye NJ, Liu GH, Zeng B, Liu SC. FINet: Dual branches feature interaction for partial-to-partial point cloud registration. In: Proc. of
the 36th AAAI Conf. on Artificial Intelligence. Virtually: AAAI, 2022. 2848–2856. [doi: 10.1609/aaai.v36i3.20189]
[33] Fu KX, Luo JZ, Luo XY, Liu SL, Zhang CX, Wang MN. Robust point cloud registration framework based on deep graph matching. IEEE
Trans. on Pattern Analysis and Machine Intelligence, 2023, 45(5): 6183–6195. [doi: 10.1109/TPAMI.2022.3204713]
[34] Yew ZJ, Lee GH. REGTR: End-to-end point cloud correspondences with Transformers. In: Proc. of the 2022 IEEE/CVF Conf. on
Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022. 6667–6676. [doi: 10.1109/CVPR52688.2022.00656]
[35] Mei GF, Poiesi F, Saltori C, Zhang J, Ricci E, Sebe N. Overlap-guided Gaussian mixture models for point cloud registration. In: Proc. of
the 2023 IEEE/CVF Winter Conf. on Applications of Computer Vision. Waikoloa: IEEE, 2023. 4500–4509. [doi: 10.1109/WACV56688.
2023.00449]