[2] [2] SCHIEBENER D, SCHMIDT A, VAHRENKAMP N, et al. Heuristic 3D object shape completion based on symmetry and scene context [C]//IEEE. Proceedings of 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Daejeon: IEEE, 2016: 74-81.

[3] [3] FIGUEIREDO R, MORENO P, BERNARDINO A. Automatic object shape completion from 3d point clouds for object manipulation [C]//IEEE.Proceedings of International Joint Conference on Computer Vision Theory and Applications. Porto: IEEE, 2017: 565-570.

[4] [4] LIU Z, TANG H, LIN Y, et al. Point-voxel enn for efficient 3d deep learning[C]//IEEE. Proceedings of Thirty-third Conference on Neural Information Processing Systems 2019. Vancouver: IEEE, 2019: 965-975.

[5] [5] SHARMA A, GRAU O, FRITZ M. Vconv-dae: deep volumetric shape learning without object labels[C]//ECCV Workshops. Proceedings of European Conference on Computer Vision 2016. Amsterdam : ECCV Workshops, 2016: 236-250.

[6] [6] YUAN W, KHOT T, HELD D, et al. Pen: point completion network[C]//IEEE. Proceedings of International Conference on 3D Vision (2018).Verona: IEEE, 2018: 728-737.

[7] [7] YANG Y, FENG C, SHEN Y, et al. FoldingNet: interpretable unsupervised learning on 3d point clouds [C]//IEEE. Proceedings of Conference on Computer Vision and Pattern Recognition(CVPR). Honolulu: IEEE, 2017:1-14.

[8] [8] TCHAPMI L P, KOSARAJU V, REZATOFIGHI H, et al. TopNet:structural point cloud decoder [C]//IEEE. Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach: IEEE, 2019: 383-392.

[9] [9] XIE H, YAO H, ZHOU S, et al. Grnet: gridding residual network for dense point cloud completion[C]//ECCV Workshops. Proceedings of European Conference on Computer Vision 2020. Glasgow, ECCV Workshops,2020: 365-381.

[10] [10] GUO M H, CAI J X, LIU Z N, et al. PCT: point cloud transformer[J].Computational Visual Media, 2021, 7(2): 187-199.

[11] [11] YU X, RAO Y, WANG Z, et al. PoinTr: diverse point cloud completion with geometry-aware transformers [C]// IEEE. Proceedings of CVF International Conference on Computer Vision (ICCV). Montreal: IEEE, 2021:12478-12487.

[12] [12] TOUVRON H, BOJANOWSKI P, CARON M, et al. Resmlp: feedforward networks for image classification with data-efficient training [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 45 (4):5314-5321.

[13] [13] ZHANG K, HAO M, WANG J, et al. Linked dynamic graph enn: Learning on point cloud via linking hierarchical features [C]//IEEE. Proceedings of International Conference on Mechatronics and Machine Vision in Practice (M2VIP). Shanghai: IEEE, 2021: 7-12.

[14] [14] FAN H, SU H, GUIBAS L J. A point set generation network for 3d object reconstruction from a single image [C]//IEEE. Proceedings of Conference on Computer Vision and Pattern Recognition(CVPR). Honolulu: IEEE,2017: 2463-2471.

[15] [15] QI C R, YI L, SU H, et al. Pointnet++: deep hierarchical feature learning on point sets in a metric space [C]//IEEE. Proceedings of 31st international conference on neural information processing systems. Long Beach:IEEE, 2017: 5105-5114.