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Transformers have been at the heart of the Natural Language Processing (NLP) and Computer Vision (CV) revolutions.
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A. Zeng, S. Song, M. Nießner, M. Fisher, J. Xiao, and T. Funkhouser, “3DMatch: Learning local geometric descriptors from RGB-D reconstructions,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2017, pp. 1802–1811
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B. Yang, X. Fu, N. D. Sidiropoulos, and M. Hong, “Towards k-means-friendly spaces: Simultaneous deep learning and clustering,” in Proc. Int. Conf. Mach. Learn. , 2017, pp. 3861–3870
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S. Xie, S. Liu, Z. Chen, and Z. Tu, “Attentional shapecontextnet for point cloud recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2018, pp. 4606–4615
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B. Graham, M. Engelcke, and L. Van Der Maaten, “3D semantic segmentation with submanifold sparse convolutional networks,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2018, pp. 9224–9232
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W. Yuan, T. Khot, D. Held, C. Mertz, and M. Hebert, “PCN: Point completion network,” in 3DV , 2018, pp. 728–737
2018
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Y. Xu, T. Fan, M. Xu, L. Zeng, and Y. Qiao, “SpiderCNN: Deep learning on point sets with parameterized convolutional filters,” in Eur. Conf. Comput. Vis. , 2018, pp. 87–102
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Y. Li, R. Bu, M. Sun, W. Wu, X. Di, and B. Chen, “PointCNN: Convolution on X-transformed points,” in Proc. Adv. Neural Inf. Process. Syst. , 2018, pp. 828–838
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M. Gadelha, R. Wang, and S. Maji, “Multiresolution tree networks for 3D point cloud processing,” in Proc. Eur. Conf. Comput. Vis. , 2018, pp. 103–118
2018
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P. Achlioptas, O. Diamanti, I. Mitliagkas, and L. Guibas, “Learning representations and generative models for 3D point clouds,” in Proc. Int. Conf. Mach. Learn. , 2018, pp. 40–49
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Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon, “Dynamic graph CNN for learning on point clouds,” ACM Trans. Graph. , vol. 38, no. 5, pp. 1–12, 2019
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J. Yang et al. , “Modeling point clouds with self-attention and gumbel subset sampling,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2019, pp. 3323–3332
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Y. Wang and J. M. Solomon, “Deep closest point: Learning representations for point cloud registration,” in Proc. IEEE Int. Conf. Comput. Vis. , 2019, pp. 3523–3532
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J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: pre-training of deep bidirectional transformers for language understanding,” in NAACL-HLT , 2019, pp. 4171–4186
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C. Choy, J. Gwak, and S. Savarese, “4D spatio-temporal convnets: Minkowski convolutional neural networks,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2019, pp. 3075–3084
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J. Lee et al. , “Set transformer: A framework for attention-based permutation-invariant neural networks,” in Proc. Int. Conf. Mach. Learn. , 2019, pp. 3744–3753
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P. Ramachandran, N. Parmar, A. Vaswani, I. Bello, A. Levskaya, and J. Shlens, “Stand-alone self-attention in vision models,” in Proc. Adv. Neural Inf. Process. Syst. , 2019, pp. 68–80
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C. R. Qi, O. Litany, K. He, and L. J. Guibas, “Deep hough voting for 3D object detection in point clouds,” in Proc. IEEE Int. Conf. Comput. Vis. , 2019, pp. 9277–9286
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J. Liu, A. Shahroudy, M. Perez, G. Wang, L.-Y. Duan, and A. C. Kot, “NTU RGB+D 120: A large-scale benchmark for 3D human activity understanding,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 42, no. 10, pp. 2684–2701, 2019
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H. Zhao, L. Jiang, C.-W. Fu, and J. Jia, “PointWeb: Enhancing local neighborhood features for point cloud processing,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2019, pp. 5565–5573
2019
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W. Wu, Z. Qi, and L. Fuxin, “PointConv: Deep convolutional networks on 3D point clouds,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2019, pp. 9621–9630
2019
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X. Liu, Z. Han, Y.-S. Liu, and M. Zwicker, “Point2Sequence: Learning the shape representation of 3D point clouds with an attention-based sequence to sequence network,” in Proc. AAAI Conf. Artif. Intell. , vol. 33, no. 01, 2019, pp. 8778–8785
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J. Mao, X. Wang, and H. Li, “Interpolated convolutional networks for 3D point cloud understanding,” in Proc. IEEE Int. Conf. Comput. Vis. , 2019, pp. 1578–1587
2019
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Z. Zhang, B.-S. Hua, and S.-K. Yeung, “ShellNet: Efficient point cloud convolutional neural networks using concentric shells statistics,” in Proc. IEEE Int. Conf. Comput. Vis. , 2019, pp. 1607–1616
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N. Carion et al. , “End-to-end object detection with transformers,” in Proc. Eur. Conf. Comput. Vis. , vol. 12346, 2020, pp. 213–229
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A. Dosovitskiy et al. , “An image is worth 16x16 words: Transformers for image recognition at scale,” in Proc. Int. Conf. Learn. Represent. , 2020, pp. 1–12
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X. Yan, C. Zheng, Z. Li, S. Wang, and S. Cui, “PointASNL: Robust point clouds processing using nonlocal neural networks with adaptive sampling,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2020, pp. 5589–5598
2020
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Q. Xie et al. , “MLCVNet: Multi-level context votenet for 3D object detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2020, pp. 10 447–10 456
2020
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H. Zhao, J. Jia, and V. Koltun, “Exploring self-attention for image recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2020, pp. 10 076–10 085
2020
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G. Trappolini, L. Cosmo, L. Moschella, R. Marin, S. Melzi, and E. Rodolà, “Shape registration in the time of transformers,” in Proc. Adv. Neural Inf. Process. Syst. , 2021, pp. 5731–5744
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2021
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Q. Xie, Y.-K. Lai, J. Wu, Z. Wang, Y. Zhang, K. Xu, and J. Wang, “Vote-based 3D object detection with context modeling and SOB-3DNMS,” Int. J. Comput. Vis. , vol. 129, no. 6, pp. 1857–1874, 2021
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S. Huang, Z. Gojcic, M. Usvyatsov, A. Wieser, and K. Schindler, “PREDATOR: Registration of 3D point clouds with low overlap,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2021, pp. 4267–4276
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2020
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Y. Lin et al. , “FPConv: Learning local flattening for point convolution,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2020, pp. 4293–4302
2020
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H. Zhao, L. Jiang, J. Jia, P. H. Torr, and V. Koltun, “Point transformer,” in Proc. IEEE Int. Conf. Comput. Vis. , 2021, pp. 16 259–16 268
2021
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M.-H. Guo, J.-X. Cai, Z.-N. Liu, T.-J. Mu, R. R. Martin, and S.-M. Hu, “PCT: Point cloud transformer,” Comput. Vis. Media. , vol. 7, no. 2, pp. 187–199, 2021
2021
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H. Wang, Y. Zhu, H. Adam, A. Yuille, and L.-C. Chen, “Max-deeplab: End-to-end panoptic segmentation with mask transformers,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2021, pp. 5463–5474
2021
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X. Chen, B. Yan, J. Zhu, D. Wang, X. Yang, and H. Lu, “Transformer tracking,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2021, pp. 8126–8135
2021
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W. Wang et al. , “Pyramid vision transformer: A versatile backbone for dense prediction without convolutions,” in Proc. IEEE Int. Conf. Comput. Vis. , 2021, pp. 548–558
2021
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H. Wu et al. , “CvT: Introducing convolutions to vision transformers,” in Proc. IEEE Int. Conf. Comput. Vis. , 2021, pp. 22–31
2021
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D. Lee et al. , “Regularization strategy for point cloud via rigidly mixed sample,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2021, pp. 15 900–15 909
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M. Xu, R. Ding, H. Zhao, and X. Qi, “PAConv: Position adaptive convolution with dynamic kernel assembling on point clouds,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2021, pp. 3173–3182
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H. Ran, W. Zhuo, J. Liu, and L. Lu, “Learning inner-group relations on point clouds,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2021, pp. 15 477–15 487
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T. Xiang, C. Zhang, Y. Song, J. Yu, and W. Cai, “Walk in the cloud: Learning curves for point clouds shape analysis,” in Proc. IEEE Int. Conf. Comput. Vis. , 2021, pp. 915–924
2021
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L. Zhu et al. , “Learning the superpixel in a non-iterative and lifelong manner,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2021, pp. 1225–1234
2021
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L. Hui, J. Yuan, M. Cheng, J. Xie, X. Zhang, and J. Yang, “Superpoint network for point cloud oversegmentation,” in Proc. IEEE Int. Conf. Comput. Vis. , 2021, pp. 5510–5519
2021
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K. Han et al. , “A survey on vision transformer,” IEEE Trans. Pattern Anal. Mach. Intell. , 2022, doi: 10.1109/TPAMI.2022.3152247
2022
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Y. Li, T. Yao, Y. Pan, and T. Mei, “Contextual transformer networks for visual recognition,” IEEE Trans. Pattern Anal. Mach. Intell. , 2022, doi: 10.1109/TPAMI.2022.3164083
2022
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J. Xiao, X. Fu, A. Liu, F. Wu, and Z.-J. Zha, “Image de-raining transformer,” IEEE Trans. Pattern Anal. Mach. Intell. , pp. 1–18, 2022, doi: 10.1109/TPAMI.2022.3183612
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2022
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X.-F. Han, Z.-Y. He, J. Chen, and G.-Q. Xiao, “3CROSSNet: Cross-level cross-scale cross-attention network for point cloud representation,” IEEE Robotics Autom. Lett. , vol. 7, no. 2, pp. 3718–3725, 2022
2022
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X. Yu, L. Tang, Y. Rao, T. Huang, J. Zhou, and J. Lu, “Point-BERT: Pre-training 3D point cloud transformers with masked point modeling,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2022, pp. 19 313–19 322
2022
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Y. Gao, X. Liu, J. Li, Z. Fang, X. Jiang, and K. M. S. Huq, “LFT-Net: Local feature transformer network for point clouds analysis,” IEEE Trans. Intell. Transport. Syst. , 2022, doi: 10.1109/TITS.2022.3140355
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X. Xu, G. Geng, X. Cao, K. Li, and M. Zhou, “TDNet: transformer-based network for point cloud denoising,” Appl. Opt. , vol. 61, no. 6, pp. C80–C88, 2022
2022
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S. Qiu, S. Anwar, and N. Barnes, “Geometric back-projection network for point cloud classification,” IEEE Trans. Multimedia , vol. 24, pp. 1943–1955, 2022
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2022
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S. L. Chenhang He, Ruihuang Li and L. Zhang, “Voxel set transformer: A set-to-set approach to 3D object detection from point clouds,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2022, pp. 8417–8427
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Z. Fan, Z. Song, H. Liu, Z. Lu, J. He, and X. Du, “SVT-Net: Super light-weight sparse voxel transformer for large scale place recognition,” in Proc. AAAI Conf. Artif. Intell. , 2022, pp. 551–560
2022
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C. Park, Y. Jeong, M. Cho, and J. Park, “Efficient point transformer for large-scale 3D scene understanding,” 2022. [Online]. Available: https://openreview.net/forum?id=3SUToIxuIT3
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X. Lai et al. , “Stratified transformer for 3D point cloud segmentation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2022, pp. 8500–8509
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C. Zhou et al. , “PTTR: Relational 3D point cloud object tracking with transformer,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2022, pp. 8531–8540
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S. Jiayao, S. Zhou, Y. Cui, and Z. Fang, “Real-time 3D single object tracking with transformer,” IEEE Trans. Multimedia , 2022, doi: 10.1109/TMM.2022.3146714
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Y. Wang, C. Yan, Y. Feng, S. Du, Q. Dai, and Y. Gao, “STORM: Structure-based overlap matching for partial point cloud registration,” IEEE Trans. Pattern Anal. Mach. Intell. , 2022, doi: 10.1109/TPAMI.2022.3148308
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R. Gao, M. Li, S.-J. Yang, and K. Cho, “Reflective noise filtering of large-scale point cloud using transformer,” Remote Sens. , vol. 14, no. 3, p. 577, 2022
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Y. Wei, H. Liu, T. Xie, Q. Ke, and Y. Guo, “Spatial-temporal transformer for 3D point cloud sequences,” in Proc. IEEE Winter Conf. Appl. Comput. Vis. , 2022, pp. 1171–1180
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Z. Qin, H. Yu, C. Wang, Y. Guo, Y. Peng, and K. Xu, “Geometric transformer for fast and robust point cloud registration,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2022, pp. 11 143–11 152
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Z. J. Yew and G. h. Lee, “REGTR: End-to-end point cloud correspondences with transformers,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2022, pp. 6677–6686
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X. Liu, G. Xu, K. Xu, J. Wan, and Y. Ma, “Point cloud completion by dynamic transformer with adaptive neighbourhood feature fusion,” IET Comput. Vis. , 2022, doi: 10.1049/cvi2.12098
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Y. Zhou, A. Ji, and L. Zhang, “Sewer defect detection from 3D point clouds using a transformer-based deep learning model,” Autom. Constr. , vol. 136, p. 104163, 2022
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Z. Wang, Y. Wang, L. An, J. Liu, and H. Liu, “Local transformer network on 3D point cloud semantic segmentation,” Information , vol. 13, no. 4, p. 198, 2022
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S. Liu, K. Fu, M. Wang, and Z. Song, “Group-in-group relation-based transformer for 3D point cloud learning,” Remote Sens. , vol. 14, no. 7, p. 1563, 2022
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C.-K. Yang, J.-J. Wu, K.-S. Chen, Y.-Y. Chuang, and Y.-Y. Lin, “An MIL-Derived transformer for weakly supervised point cloud segmentation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2022, pp. 11 830–11 839
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C. Park, Y. Jeong, M. Cho, and J. Park, “Fast point transformer,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2022, pp. 16 949–16 958
2022
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X. Chen, H. Zhao, G. Zhou, and Y.-Q. Zhang, “PQ-transformer: Jointly parsing 3D objects and layouts from point clouds,” IEEE Robot. Autom. Lett. , vol. 7, no. 2, pp. 2519–2526, 2022
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Y. Zhang, J. Chen, and D. Huang, “CAT-Det: Contrastively augmented transformer for multi-modal 3D object detection,” Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2022
2022
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Y. Wang et al. , “Bridged transformer for vision and point cloud 3D object detection,” Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , pp. 12 114–12 123, 2022
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X. Bai et al. , “TransFusion: robust lidar-camera fusion for 3D object detection with transformers,” Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , pp. 1090–1099, 2022
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S. Huang, Z. Yang, Y. Shi, J. Tan, H. Li, and Y. Cheng, “3DPCTN: Two 3D local-object point-cloud-completion transformer networks based on self-attention and multi-resolution,” Electronics , vol. 11, no. 9, p. 1351, 2022
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X. Wen et al. , “PMP-Net++: Point cloud completion by transformer-enhanced multi-step point moving paths,” IEEE Trans. Pattern Anal. Mach. Intell. , 2022
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