[1] Sun J K, Yang Z H, Li F F et al. Projected feature assisted coarse to fine point cloud registration method for large-size 3D measurement[J]. Optics Express, 31, 18379-18398(2023).

[2] Chen B W, Shi S, Sun J et al. Hyperspectral lidar point cloud segmentation based on geometric and spectral information[J]. Optics Express, 27, 24043-24059(2019).

[3] Feng W, Qu T, Gao J H et al. 3D reconstruction of structured light fields based on point cloud adaptive repair for highly reflective surfaces[J]. Applied Optics, 60, 7086-7093(2021).

[4] Wang G, Le N M, Hu X H et al. Semi-automated registration and segmentation for gingival tissue volume measurement on 3D OCT images[J]. Biomedical Optics Express, 11, 4536-4547(2020).

[5] Liang B Y, Wang S M, Shen F et al. Acoustic impact of the human skull on transcranial photoacoustic imaging[J]. Biomedical Optics Express, 12, 1512-1528(2021).

[6] Yuan W T, Khot T, Held D et al. PCN: point completion network[C], 728-737(2018).

[7] Chang A X, Funkhouser T, Guibas L et al. ShapeNet: an information-rich 3D model repository[EB/OL]. https://arxiv.org/abs/1512.03012

[8] Dai A, Charles R Q, Nießner M. Shape completion using 3D-encoder-predictor CNNs and shape synthesis[C], 6545-6554(2017).

[9] Han X G, Li Z, Huang H B et al. High-resolution shape completion using deep neural networks for global structure and local geometry inference[C], 85-93(2017).

[10] Sharma A, Grau O, Fritz M, Hua G, Jégou H. VConv-DAE: deep volumetric shape learning without object labels[M]. Computer vision-ECCV 2016 workshops. Lecture notes in computer science, 9915, 236-250(2016).

[11] Xie H Z, Yao H X, Zhou S C, Ferrari V, Hebert M, Sminchisescu C et al. GRNet: gridding residual network for dense point cloud completion[M]. Computer vision-ECCV 2018. Lecture notes in computer science, 12345, 365-381(2020).

[12] Charles R Q, Hao S, Mo K C et al. PointNet: deep learning on point sets for 3D classification and segmentation[C], 77-85(2017).

[13] Tchapmi L P, Kosaraju V, Rezatofighi H et al. TopNet: structural point cloud decoder[C], 383-392(2020).

[14] Liu M H, Sheng L, Yang S et al. Morphing and sampling network for dense point cloud completion[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 11596-11603(2020).

[15] Wen X, Xiang P, Han Z Z et al. PMP-Net: point cloud completion by learning multi-step point moving paths[C], 7439-7448(2021).

[16] Xiang P, Wen X, Liu Y S et al. SnowflakeNet: point cloud completion by snowflake point deconvolution with skip-transformer[C], 5479-5489(2022).

[17] Wang J, Cui Y, Guo D Y et al. PointAttN: You only need attention for point cloud completion[EB/OL]. https://arxiv.org/abs/2203.08485

[18] Yang Y Q, Feng C, Shen Y R et al. FoldingNet: point cloud auto-encoder via deep grid deformation[C], 206-215(2018).

[19] Wang X G, Ang M H, Lee G H. Cascaded refinement network for point cloud completion[C], 787-796(2020).

[20] Yu X M, Rao Y M, Wang Z Y et al. PoinTr: diverse point cloud completion with geometry-aware transformers[C], 12478-12487(2022).

[21] Gao H, Yang Z J, Ling Y Q et al. Point cloud completion network based on multiencoders and residual-transformer[J]. Laser & Optoelectronics Progress, 60, 0210012(2023).

[22] Luo K Q, Zhu J P, Zhou P et al. Point cloud completion network based on multibranch structure[J]. Laser & Optoelectronics Progress, 57, 241019(2020).

[23] Vaswani A, Shazeer N, Parmar N et al. Attention is all You need[C], 6000-6010(2017).

[24] Charles R Q, Yi L, Su H et al. PointNet++: deep hierarchical feature learning on point sets in a metric space[EB/OL]. https://arxiv.org/abs/1706.02413

[25] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C], 7132-7141(2018).