| ［1］ |
ZENG Y M， HU Y， LIU S C， et al. RT3D: Real-time 3-D vehicle detection in LiDAR point cloud for autonomous driving［J］. IEEE Robotics and Automation Letters， 2018，3（4）:3434-3440.
|
| ［2］ |
RAHMAN M M， TAN Y H， XUE J， et al. Recent advances in 3D object detection in the era of deep neural networks: A survey［J］. IEEE Transactions on Image Processing， 2020，29:2947-2962.
|
| ［3］ |
HU Q Y， YANG B， XIE L H， et al. RandLA-Net: Efficient semantic segmentation of large-scale point clouds［C］// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. 2020:11105-11114.
|
| ［4］ |
HAN X F， LAGA H， BENNAMOUN M. Image-based 3D object reconstruction: State-of-the-art and trends in the deep learning era［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2021，43（5）:1578-1604.
|
| ［5］ |
HU W， FU Z Q， GUO Z M. Local frequency interpretation and non-local self-similarity on graph for point cloud inpainting［J］. IEEE Transactions on Image Processing， 2019，28（8）:4087-4100.
|
| ［6］ |
MARTINOVIC A， GOOL L V. Bayesian grammar learning for inverse procedural modeling［C］// 2013 IEEE Conference on Computer Vision and Pattern Recognition. 2013:201-208.
|
| ［7］ |
YUAN W T， KHOT T， HELD D， et al. PCN: Point completion network［C］// 2018 International Conference on 3D Vision （3DV）. 2018:728-737.
|
| ［8］ |
HUANG Z T， YU Y K， XU J W， et al. PF-Net: Point fractal network for 3D point cloud completion［C］// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. 2020:7659-7667.
|
| ［9］ |
YU X M， RAO Y M， WANG Z Y， et al. PoinTr: Diverse point cloud completion with geometry-aware transformers.［C］// 2021 IEEE/CVF International Conference on Computer Vision （ICCV）. 2021: 12478-12487.
|
| ［10］ |
YI L， KIM V G， CEYLAN D， et al. A scalable active framework for region annotation in 3D shape collections［J］. ACM Transactions on Graphics， 2016，35（6）. DOI:10.1145/2980179.2980238
|
| ［11］ |
HAN Z Z， LIU Z B， HAN J W， et al. Mesh convolutional restricted Boltzmann machines for unsupervised learning of features with structure preservation on 3-D meshes［J］. IEEE Transactions on Neural Networks and Learning Systems， 2017，28（10）:2268-2281.
|
| ［12］ |
LIU X H， HAN Z Z， LIU Y S， et al. Point2Sequence: Learning the shape representation of 3D point clouds with an attention-based sequence to sequence network［J］. Proceedings of the AAAI Conference on Artificial Intelligence， 2019，33（1）:8778-8785.
|
| ［13］ |
HAN Z Z， LIU Z B， VONG C M， et al. BoSCC: Bag of spatial context correlations for spatially enhanced 3D shape representation［J］. IEEE Transactions on Image Processing， 2017，26（8）: 3707-3720.
|
| ［14］ |
HAN Z Z， LIU X H， LIU Y S， et al. Parts4Feature: Learning 3D global features from generally semantic parts in multiple views［J］. arXiv preprint arXiv:1905.07506， 2019.
|
| ［15］ |
WANG Y， SOLOMON J. Deep closest point: Learning representations for point cloud registration［C］// 2019 IEEE/CVF International Conference on Computer Vision （ICCV）. 2019:3522-3531.
|
| ［16］ |
HAN Z Z， SHANG M Y， LIU Y S， et al. View Inter-prediction GAN: Unsupervised representation learning for 3D shapes by learning global shape memories to support local view predictions［J］. Proceedings of the AAAI Conference on Artificial Intelligence， 2019，33（1）:8376-8384.
|
| ［17］ |
HAN Z Z， LIU H L， LIU Z B， et al. 3D2SeqViews: Aggregating sequential views for 3D global feature learning by CNN with hierarchical attention aggregation［J］. IEEE Transactions on Image Processing， 2019，28（8）:3986-3999.
|
| ［18］ |
LAWIN F J， DANELLJAN M， TOSTEBERG P， et al. Deep projective 3D semantic segmentation［C］// International Conference on Computer Analysis of Images and Patterns. 2017:95-107.
|
| ［19］ |
BEN-SHABAT Y， LINDENBAUM M， FISCHER A. 3DmFV: Three-dimensional point cloud classification in real-time using convolutional neural networks.［J］. IEEE Robotics and Automation Letters， 2018，3（4）:3145-3152.
|
| ［20］ |
CHARLES R Q， SU H， KAICHUN M， et al. PointNet: Deep learning on point sets for 3D classification and segmentation［C］// 2017 IEEE Conference on Computer Vision and Pattern Recognition （CVPR）. 2017:77-85.
|
| ［21］ |
QI C R， YI L， SU H， et al. PointNet++: Deep hierarchical feature learning on point sets in a metric space［C］// Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017:5105-5114.
|
| ［22］ |
YANG Y Q， FENG C， SHEN Y R， et al. FoldingNet: Point cloud auto-encoder via deep grid deformation［C］// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2018:206-215.
|
| ［23］ |
ZHANG J Z， CHEN X Y， CAI Z G， et al. Unsupervised 3D shape completion through GAN inversion［C］// 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. 2021:1768-1777.
|
| ［24］ |
BARSOUM E， KENDER JOHN， LIU Z C. HP-GAN: Probabilistic 3D human motion prediction via GAN［C］// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops （CVPRW）. 2018:1499-1508.
|
| ［25］ |
ZHANG X C， FENG Y T， LI S Q， et al. View-guided point cloud completion［C］// 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. 2021:15885-15894.
|
| ［26］ |
WOLF T， DEBUT L， SANH V， et al. Transformers: State-of-the-art natural language processing［C］// Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. 2020:38-45.
|
| ［27］ |
WANG Y， SUN Y B， LIU Z W， et al. Dynamic graph CNN for learning on point clouds［J］. ACM Transactions on Graphic， 2019，38（5）. DOI: 10.1145/3326362.
|
| ［28］ |
FAN H Q， SU H， GUIBAS L J. A point set generation network for 3D object reconstruction from a single image［J］. arXiv preprint arXiv:1612.00603v2， 2016.
|
