三维场景点云理解与重建技术

龚靖渝; 楼雨京; 柳奉奇; 张志伟; 陈豪明; 张志忠; 谭鑫; 谢源; 马利庄

doi:10.11834/jig.230004

目标检测与图像重建 | 浏览量 : 0 下载量: 496 CSCD: 5

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专辑

三维场景点云理解与重建技术
Scene point cloud understanding and reconstruction technologies in 3D space
2023年28卷第6期页码：1741-1766
收稿日期：2023-01-03，

修回日期：2023-02-21，

纸质出版日期：2023-06-16
DOI： 10.11834/jig.230004
稿件说明：

移动端阅览

龚靖渝，楼雨京，柳奉奇，张志伟，陈豪明，张志忠，谭鑫，谢源，马利庄. 2023. 三维场景点云理解与重建技术. 中国图象图形学报， 28(06):1741-1766 DOI： 10.11834/jig.230004.

Gong Jingyu， Lou Yujing， Liu Fengqi， Zhang Zhiwei， Chen Haoming， Zhang Zhizhong， Tan Xin， Xie Yuan， Ma Lizhuang. 2023. Scene point cloud understanding and reconstruction technologies in 3D space. Journal of Image and Graphics， 28(06):1741-1766 DOI： 10.11834/jig.230004.

摘要

3维场景理解与重建技术能够使计算机对真实场景进行高精度复现并引导机器以3维空间的思维理解整个真实世界，从而使机器拥有足够智能参与到真实世界的生产与建设，并能通过场景的模拟为人类的决策和生活提供服务。3维场景理解与重建技术主要包含场景点云特征提取、扫描点云配准与融合、场景理解与语义分割、扫描物体点云补全与细粒度重建等，在处理真实扫描场景时，受到扫描设备、角度、距离以及场景复杂程度的影响，对技术的精准度和稳定性提出了更高的要求，相关的技术也十分具有挑战性。其中，原始扫描点云特征提取与配准融合旨在将同场景下多个扫描区域进行特征匹配，从而融合得到完整的场景点云，是理解与重建技术的基石；场景点云的理解与语义分割的目的在于对场景模型进行整体感知并根据语义特征划分为功能性物体甚至是部件的点云，是整套技术的核心组成部分；后续的物体点云细粒度补全主要研究扫描物体的结构恢复和残缺部分补全，是场景物体点云细粒度重建的关键性技术。本文围绕上述系列技术，详细分析了基于3维点云的场景理解与重建技术相关的应用领域和研究方向，归结总结了国内外的前沿进展与研究成果，对未来的研究方向和技术发展进行了展望。

Abstract

3D scene understanding and reconstruction are essential for machine vision and intelligence， which aim to reconstruct completed models of real scenes from multiple scene scans and understand the semantic meanings of each functional component in the scene. This technique is indispensable for real world digitalization and simulation， which can be widely used in related domains like robots， navigation system and virtual tourism. Its key challenges are required to be resolved on the three aspects： 1） to recognize the same area in multiple real scans and fuse all the scans into an integrated scene point cloud； 2） to make sense of the whole scene and recognize the semantics of multiple functional components； 3） to complete the missing region in the original point cloud caused by occlusion during scanning. It is necessary to extract point cloud feature in order to fuse multiple real scene scans into an integrated point cloud， which can be invariant to scanning position and rotation. Thus， intrinsic geometry features like point distance and singular value in neighborhood covariance matrix are often involved in rotation-invariant feature design. Contrastive learning scheme is usually taken to help the learned features from the same area to be close to each other， while extracted features from different areas to be far away. To get generalization ability better， data augmentation of scanned point cloud can also be used during feature learning process. Features-learnt pose estimation of scanning device can be configured to calculate the transformation matrix between point cloud pairs. After the transformation relationship is sorted out， the following point cloud fusion can be implemented using the raw point cloud scans. To further understand raw point cloud-based whole scene and segment the whole scene into functional parts on the basis of multiple semantics， an effective and efficient network with appropriate 3D convolution operation is required to parse entire points-based scene hierarchically， and specific learning schemes are necessary as well to adapt to various situation. The definition and formulation of basic convolution operation in 3D space is recognized as the core of pattern recognition for 3D scene point cloud. It is highly correlated to the approximated convolution kernel in 3D space where feature extraction can be developed in terms of appropriate point cloud grouping and down/up-sampling. The discrete approximation of 3D continuous convolution pursues being capable of recognizing various geometry pattern while keeping as few parameters as possible. Network design based on these elementary 3D convolution operations is also a fundamental part of outstanding scene parsing. Furthermore， point-level semantic segmentation of scanned scene can be linked mutually in relevance to such aspects of boundary detection， instance segmentation， and scene coloring， where network parameters are supervised through more auxiliary regularization. Semi-supervised methods and weak-supervised methods are required to overcome the lack of data annotation for real data. The segmentation results and semantic hints can be used to strengthen the fine-grained completion of object point cloud from scanned scene， in which the segmented objects can be handled separately， and semantics can be used to provide the structure and geometry prior when occlusion-derived missing region is completed. For the learning of object point cloud completion， it is crucial to learn a compact latent code space to represent all the complete shapes and design versatile decoder to reconstruct the structure and fine-grained geometry details of object point cloud. The learnt latent code space should contain complete shapes as much as possible， thus requiring large-scale synthetic model dataset for training to ensure the generalization ability. The encoder should be designed to recognize the structure of original point cloud and extract specific geometry pattern which preserves this information in latent code， while the decoder is used to recover the overall skeleton of original scanned objects and complete all the details according to the existing local geometry hints. For real scanned object completion， it is required to optimize the integration of latent code space further for synthetic models and real scanned point cloud. A cross-domain learning scheme is used to apply the knowledge of completion to real object scans， whereas the details of real scanned object can be preserved in the completed version. We analyze the current situation about scene understanding and reconstruction， including point cloud fusion， 3D convolution operation， entire scene segmentation， and fine-grained object completion. We analyze the frontier technologies and predict promising future research trends. It is significant for the following research to pay more attention on more open space with further challenges on computing efficiency， handling out-of-domain knowledge， and more complex situation with human-scene interaction. The 3D scene understanding and reconstruction technology will help the machine to understand the real world in a more natural way which can facilitate such various application domains like robots and navigation. It also potential to conduct plausible simulation of real world based on the reconstruction and parsing of real scenes， making it a useful tool in making various decisions.

关键词

Keywords

references

Achlioptas P ， Diamanti O ， Mitliagkas I and Guibas L . 2018 . Learning representations and generative models for 3D point clouds // Proceedings of the 35th International Conference on Machine Learning . Stockholm， Sweden ： PMLR： 40 - 49

Afham M ， Dissanayake I ， Dissanayake D ， Dharmasiri A ， Thilakarathna K and Rodrigo R . 2022 . CrossPoint： self-supervised cross-modal contrastive learning for 3D point cloud understanding // Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition . New Orleans， USA ： IEEE： 9892 - 9902 ［ DOI： 10.1109/CVPR52688.2022.00967 http://dx.doi.org/10.1109/CVPR52688.2022.00967 ］

Ao S ， Hu Q Y ， Yang B ， Markham A and Guo Y L . 2021 . SpinNet： learning a general surface descriptor for 3D point cloud registration // Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Nashville， USA ： IEEE： 11748 - 11757 ［ DOI： 10.1109/CVPR46437.2021.01158 http://dx.doi.org/10.1109/CVPR46437.2021.01158 ］

Armeni I ， Sener O ， Zamir A R ， Jiang H ， Brilakis I ， Fischer M and Savarese S . 2016 . 3D semantic parsing of large-scale indoor spaces // Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas， USA ： IEEE： 1534 - 1543 ［ DOI： 10.1109/CVPR.2016.170 http://dx.doi.org/10.1109/CVPR.2016.170 ］

Bai X Y ， Luo Z X ， Zhou L ， Fu H B ， Quan L and Tai C L . 2020 . D3Feat： joint learning of dense detection and description of 3D local features // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Seattle， USA ： IEEE： 6358 - 6366 ［ DOI： 10.1109/CVPR42600.2020.00639 http://dx.doi.org/10.1109/CVPR42600.2020.00639 ］

Behley J ， Garbade M ， Milioto A ， Quenzel J ， Behnke S ， Stachniss C and Gall J . 2019 . SemanticKITTI： a dataset for semantic scene understanding of LiDAR sequences // Proceedings of 2019 IEEE/CVF International Conference on Computer Vision . Seoul， Korea （South）： IEEE： 9296 - 9306 ［ DOI： 10.1109/ICCV.2019.00939 http://dx.doi.org/10.1109/ICCV.2019.00939 ］

Bian Y K ， Hui L ， Qian J J and Xie J . 2022 . Unsupervised domain adaptation for point cloud semantic segmentation via graph matching // Proceedings of 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems . Kyoto， Japan ： IEEE： 9899 - 9904 ［ DOI： 10.1109/IROS47612.2022.9981603 http://dx.doi.org/10.1109/IROS47612.2022.9981603 ］

Bronstein M M ， Bruna J ， LeCun Y ， Szlam A and Vandergheynst P . 2017 . Geometric deep learning： going beyond Euclidean data . IEEE Signal Processing Magazine ， 34 （ 4 ）： 18 - 42 ［ DOI： 10.1109/MSP.2017.2693418 http://dx.doi.org/10.1109/MSP.2017.2693418 ］

Caesar H ， Bankiti V ， Lang A H ， Vora S ， Liong V E ， Xu Q ， Krishnan A ， Pan Y ， Baldan G and Beijbom O . 2020 . nuScenes： a multimodal dataset for autonomous driving // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Seattle， USA ： IEEE： 11618 - 11628 ［ DOI： 10.1109/CVPR42600.2020.01164 http://dx.doi.org/10.1109/CVPR42600.2020.01164 ］

Cai Y J ， Lin K Y ， Zhang C ， Wang Q ， Wang X G and Li H S . 2022 . Learning a structured latent space for unsupervised point cloud completion // Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition . New Orleans， USA ： IEEE： 5533 - 5543 ［ DOI： 10.1109/CVPR52688.2022.00546 http://dx.doi.org/10.1109/CVPR52688.2022.00546 ］

Chang A X ， Funkhouser T ， Guibas L ， Hanrahan P ， Huang Q X ， Li Z M ， Savarese S ， Savva M ， Song S R ， Su H ， Xiao J X ， Yi L and Yu F . 2015 . ShapeNet： an information-rich 3D model repository ［EB/OL］. ［ 2023-01-03 ］. https://arxiv.org/pdf/1512.03012.pdf https://arxiv.org/pdf/1512.03012.pdf

Chen C ， Li G B ， Xu R J ， Chen T S ， Wang M and Lin L . 2019 . ClusterNet： deep hierarchical cluster network with rigorously rotation-invariant representation for point cloud analysis // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Long Beach， USA ： IEEE： 4989 - 4997 ［ DOI： 10.1109/CVPR.2019.00513 http://dx.doi.org/10.1109/CVPR.2019.00513 ］

Chen H and Bhanu B . 2007 . 3D free-form object recognition in range images using local surface patches . Pattern Recognition Letters ， 28 （ 10 ）： 1252 - 1262 ［ DOI： 10.1016/j.patrec.2007.02.009 http://dx.doi.org/10.1016/j.patrec.2007.02.009 ］

Chen S Y ， Fang J M ， Zhang Q ， Liu W Y and Wang X G . 2021 . Hierarchical aggregation for 3D instance segmentation // Proceedings of 2021 IEEE/CVF International Conference on Computer Vision . Montreal， Canada ： IEEE： 15447 - 15456 ［ DOI： 10.1109/ICCV48922.2021.01518 http://dx.doi.org/10.1109/ICCV48922.2021.01518 ］

Chen S Y ， Wang X G ， Cheng T H ， Zhang W Q ， Zhang Q ， Huang C and Liu W Y . 2022 . AziNorm： exploiting the radial symmetry of point cloud for azimuth-normalized 3D perception // Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition . New Orleans， USA ： IEEE： 6377 - 6386 ［ DOI： 10.1109/CVPR52688.2022.00628 http://dx.doi.org/10.1109/CVPR52688.2022.00628 ］

Chen X L ， Chen B Q and Mitra N J . 2020 . Unpaired point cloud completion on real scans using adversarial training // Proceedings of the 8th International Conference on Learning Representations . Addis Ababa， Ethiopia ： OpenReview.net

Choy C ， Gwak J and Savarese S . 2019a . 4D spatio-temporal ConvNets： minkowski convolutional neural networks // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Long Beach， USA ： IEEE： 3070 - 3079 ［ DOI： 10.1109/CVPR.2019.00319 http://dx.doi.org/10.1109/CVPR.2019.00319 ］

Choy C ， Park J and Koltun V . 2019b . Fully convolutional geometric features // Proceedings of 2019 IEEE/CVF International Conference on Computer Vision . Seoul， Korea （South）： IEEE： 8957 - 8965 ［ DOI： 10.1109/ICCV.2019.00905 http://dx.doi.org/10.1109/ICCV.2019.00905 ］

Cortinhal T ， Tzelepis G and Erdal Aksoy E . 2020 . SalsaNext： fast， uncertainty-aware semantic segmentation of LiDAR point clouds // Proceedings of the 15th International Symposium on Visual Computing . San Diego， USA ： Springer： 207 - 222 ［ DOI： 10.1007/978-3-030-64559-5_16 http://dx.doi.org/10.1007/978-3-030-64559-5_16 ］

Dai A ， Chang A X ， Savva M ， Halber M ， Funkhouser T and Nießner M . 2017 . ScanNet： richly-annotated 3D reconstructions of indoor scenes // Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition . Honolulu， USA ： IEEE： 2432 - 2443 ［ DOI： 10.1109/CVPR.2017.261 http://dx.doi.org/10.1109/CVPR.2017.261 ］

Defferrard M ， Bresson X and Vandergheynst P . 2016 . Convolutional neural networks on graphs with fast localized spectral filtering // Proceedings of the 30th International Conference on Neural Information Processing Systems . Barcelona， Spain ： Curran Associates Inc.： 3844 - 3852

Drost B ， Ulrich M ， Navab N and Ilic S . 2010 . Model globally， match locally： efficient and robust 3D object recognition // Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition . San Francisco， USA ： IEEE： 998 - 1005 ［ DOI： 10.1109/CVPR.2010.5540108 http://dx.doi.org/10.1109/CVPR.2010.5540108 ］

Feng M T ， Zhang L ， Lin X F ， Gilani S Z and Mian A . 2020 . Point attention network for semantic segmentation of 3D point clouds . Pattern Recognition ， 107 ： # 107446 ［ DOI： 10.1016/j.patcog.2020.107446 http://dx.doi.org/10.1016/j.patcog.2020.107446 ］

Flint A ， Dick A and van den Hengel A . 2007 . Thrift： local 3D structure recognition // Proceedings of the 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications （DICTA 2007） . Glenelg， Australia ： IEEE： 182 - 188 ［ DOI： 10.1109/DICTA.2007.4426794 http://dx.doi.org/10.1109/DICTA.2007.4426794 ］

Frome A ， Huber D ， Kolluri R ， Bülow T and Malik J . 2004 . Recognizing objects in range data using regional point descriptors // Proceedings of the 8th European Conference on Computer Vision . Prague， Czech Republic ： Springer： 224 - 237 ［ DOI： 10.1007/978-3-540-24672-5_18 http://dx.doi.org/10.1007/978-3-540-24672-5_18 ］

Geiger A ， Lenz P and Urtasun R . 2012 . Are we ready for autonomous driving？ The KITTI vision benchmark suite // Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition . Providence， USA ： IEEE： 3354 - 3361 ［ DOI： 10.1109/CVPR.2012.6248074 http://dx.doi.org/10.1109/CVPR.2012.6248074 ］

Gojcic Z ， Zhou C F ， Wegner J D and Wieser A . 2019 . The perfect match： 3D point cloud matching with smoothed densities // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Long Beach， USA ： IEEE： 5540 - 5549 ［ DOI： 10.1109/CVPR.2019.00569 http://dx.doi.org/10.1109/CVPR.2019.00569 ］

Gong J Y ， Liu F Q ， Xu J C ， Wang M ， Tan X ， Zhang Z Z ， Yi R ， Song H C ， Xie Y and Ma L Z . 2022 . Optimization over disentangled encoding： unsupervised cross-domain point cloud completion via occlusion factor manipulation // Proceedings of the 17th European Conference on Computer Vision . Tel Aviv， Israel ： Springer： 517 - 533 ［ DOI： 10.1007/978-3-031-20086-1_30 http://dx.doi.org/10.1007/978-3-031-20086-1_30 ］

Gong J Y ， Xu J C ， Tan X ， Song H C ， Qu Y Y ， Xie Y and Ma L Z . 2021a . Omni-supervised point cloud segmentation via gradual receptive field component reasoning // Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Nashville， USA ： IEEE： 11668 - 11677 ［ DOI： 10.1109/CVPR46437.2021.01150 http://dx.doi.org/10.1109/CVPR46437.2021.01150 ］

Gong J Y ， Xu J C ， Tan X ， Zhou J ， Qu Y Y ， Xie Y and Ma L Z . 2021b . Boundary-aware geometric encoding for semantic segmentation of point clouds // Proceedings of the 35th AAAI Conference on Artificial Intelligence . Palo Alto， USA ： AAAI： 1424 - 1432 ［ DOI： 10.1609/aaai.v35i2.16232 http://dx.doi.org/10.1609/aaai.v35i2.16232 ］

Graham B ， Engelcke M and van der Maaten L . 2018 . 3D semantic segmentation with submanifold sparse convolutional networks // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City， USA ： IEEE： 9224 - 9232 ［ DOI： 10.1109/CVPR.2018.00961 http://dx.doi.org/10.1109/CVPR.2018.00961 ］

Guo M H ， Cai J X ， Liu Z N ， Mu T J ， Martin R R and Hu S M . 2021 . PCT： point cloud transformer . Computational Visual Media ， 7 （ 2 ）： 187 - 199 ［ DOI： 10.1007/s41095-021-0229-5 http://dx.doi.org/10.1007/s41095-021-0229-5 ］

Guo Y L ， Sohel F ， Bennamoun M ， Lu M and Wan J W . 2013 . Rotational projection statistics for 3D local surface description and object recognition . International Journal of Computer Vision ， 105 （ 4 ）： 63 - 86 ［ DOI： 10.1007/s11263-013-0627-y http://dx.doi.org/10.1007/s11263-013-0627-y ］

Hackel T ， Savinov N ， Ladicky L ， Wegner J D ， Schindler K and Pollefeys M . 2017 . Semantic3D.net： a new large-scale point cloud classification benchmark ［EB/OL］. ［ 2023-01-03 ］. http://arxiv.org/pdf/1704.03847.pdf http://arxiv.org/pdf/1704.03847.pdf

Hassani K and Haley M . 2019 . Unsupervised multi-task feature learning on point clouds // Proceedings of 2019 IEEE/CVF International Conference on Computer Vision . Seoul， Korea （South）： IEEE： 8159 - 8170 ［ DOI： 10.1109/ICCV.2019.00825 http://dx.doi.org/10.1109/ICCV.2019.00825 ］

Hosseinzadeh M ， Li K J ， Latif Y and Reid I . 2019 . Real-time monocular object-model aware sparse SLAM // Proceedings of 2019 International Conference on Robotics and Automation . Montreal， Canada ： IEEE： 7123 - 7129 ［ DOI： 10.1109/ICRA.2019.8793728 http://dx.doi.org/10.1109/ICRA.2019.8793728 ］

Houston J ， Zuidhof G ， Bergamini L ， Ye Y W ， Chen L ， Jain A ， Omari S ， Iglovikov V and Ondruska P . 2020 . One thousand and one hours： self-driving motion prediction dataset // Proceedings of the 4th Conference on Robot Learning . Cambridge， USA ： PMLR： 409 - 418

Hua B S ， Tran M K and Yeung S K . 2018 . Pointwise convolutional neural networks // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City， USA ： IEEE： 984 - 993 ［ DOI： 10.1109/CVPR.2018.00109 http://dx.doi.org/10.1109/CVPR.2018.00109 ］

Jaritz M ， Vu T H ， de Charette R ， Wirbel E and Pérez P . 2020 . xMUDA： cross-modal unsupervised domain adaptation for 3D semantic segmentation // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Seattle， USA ： IEEE： 12602 - 12611 ［ DOI： 10.1109/CVPR42600.2020.01262 http://dx.doi.org/10.1109/CVPR42600.2020.01262 ］

Jiang L ， Zhao H S ， Shi S S ， Liu S ， Fu C W and Jia J Y . 2020 . PointGroup： dual-set point grouping for 3D instance segmentation // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Seattle， USA ： IEEE： 4866 - 4875 ［ DOI： 10.1109/CVPR42600.2020.00492 http://dx.doi.org/10.1109/CVPR42600.2020.00492 ］

Johnson A E and Hebert M . 1999 . Using spin images for efficient object recognition in cluttered 3D scenes . IEEE Transactions on Pattern Analysis and Machine Intelligence ， 21 （ 5 ）： 433 - 449 ［ DOI： 10.1109/34.765655 http://dx.doi.org/10.1109/34.765655 ］

Kim S ， Park J and Han B . 2020 . Rotation-invariant local-to-global representation learning for 3D point cloud // Proceedings of the 34th International Conference on Neural Information Processing Systems . Vancouver， Canada ： Curran Associates Inc.： #685

Kong L D ， Ren J W ， Pan L and Liu Z W . 2022 . LaserMix for semi-supervised LiDAR semantic segmentation ［EB/OL］. ［ 2022-06-30 ］. https://arxiv.org/pdf/2207.00026.pdf https://arxiv.org/pdf/2207.00026.pdf

Kundu A ， Genova K ， Yin X Q ， Fathi A ， Pantofaru C ， Guibas L ， Tagliasacchi A ， Dellaert F and Funkhouser T . 2022 . Panoptic neural fields： a semantic object-aware neural scene representation // Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition . New Orleans， USA ： IEEE： 12861 - 12871 ［ DOI： 10.1109/CVPR52688.2022.01253 http://dx.doi.org/10.1109/CVPR52688.2022.01253 ］

Lei H ， Akhtar N and Mian A . 2020 . SegGCN： efficient 3D point cloud segmentation with fuzzy spherical kernel // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Seattle， USA ： IEEE： 11608 - 11617 ［ DOI： 10.1109/CVPR42600.2020.0116 http://dx.doi.org/10.1109/CVPR42600.2020.0116 ］

Li F R ， Fujiwara K ， Okura F and Matsushita Y . 2021a . A closer look at rotation-invariant deep point cloud analysis // Proceedings of 2021 IEEE/CVF International Conference on Computer Vision . Montreal， Canada ： IEEE： 16198 - 16207 ［ DOI： 10.1109/ICCV48922.2021.01591 http://dx.doi.org/10.1109/ICCV48922.2021.01591 ］

Li H Y ， Sun Z X ， Wu Y J and Song Y C . 2021b . Semi-supervised point cloud segmentation using self-training with label confidence prediction . Neurocomputing ， 437 ： 227 - 237 ［ DOI： 10.1016/j.neucom.2021.01.091 http://dx.doi.org/10.1016/j.neucom.2021.01.091 ］

Li J W and Zhan J W . 2022 . Review on 3D point cloud registration method . Journal of Image and Graphics ， 27 （ 2 ）： 349 - 367

李建微，占家旺 . 2022 . 3维点云配准方法研究进展 . 中国图象图形学报， 27 （ 2 ）： 349 - 367 ［ DOI： 10.11834/jig.210243 http://dx.doi.org/10.11834/jig.210243 ］

Li J X ， Chen B M and Lee G H . 2018a . SO-Net： self-organizing network for point cloud analysis // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City， USA ： IEEE： 9397 - 9406 ［ DOI： 10.1109/CVPR.2018.00979 http://dx.doi.org/10.1109/CVPR.2018.00979 ］

Li M T ， Xie Y ， Shen Y H ， Ke B ， Qiao R Z ， Ren B ， Lin S H and Ma L Z . 2022a . HybridCR： weakly-supervised 3D point cloud semantic segmentation via hybrid contrastive regularization // Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition . New Orleans， USA ： IEEE： 14910 - 14919 ［ DOI： 10.1109/CVPR52688.2022.01451 http://dx.doi.org/10.1109/CVPR52688.2022.01451 ］

Li X Y ， Zhang G ， Pan H Y and Wang Z H . 2022b . CPGNet： cascade point-grid fusion network for real-time LiDAR semantic segmentation // Proceedings of 2022 International Conference on Robotics and Automation . Philadelphia， USA ： IEEE： 11117 - 11123 ［ DOI： 10.1109/ICRA46639.2022.9811767 http://dx.doi.org/10.1109/ICRA46639.2022.9811767 ］

Li Y Y ， Bu R ， Sun M C ， Wu W ， Di X H and Chen B Q . 2018b . PointCNN： convolution on X -transformed points // Proceedings of the 32nd International Conference on Neural Information Processing Systems . Montréal， Canada ： Curran Associates Inc.： 828 - 838

Liu C and Furukawa Y . 2019a . MASC： multi-scale affinity with sparse convolution for 3D instance segmentation ［EB/OL］. ［ 2023-01-03 ］. https://arxiv.org/pdf/1902.04478.pdf https://arxiv.org/pdf/1902.04478.pdf

Liu Q ， Jiang N J ， Lu J B ， Chen M G ， Yi R and Ma L Z . 2022 . ScatterNet： point cloud learning via scatters // Proceedings of the 30th ACM International Conference on Multimedia . Lisboa， Portugal ： ACM： 5611 - 5619 ［ DOI： 10.1145/3503161.3548354 http://dx.doi.org/10.1145/3503161.3548354 ］

Liu S ， Huang S Y ， Cheng H H ， Shen J Y and Chen S Y . 2021 . A deep residual network with spatial depthwise convolution for large-scale point cloud semantic segmentation . Journal of Image and Graphics ， 26 （ 12 ）： 2848 - 2859

刘盛，黄圣跃，程豪豪，沈家瑜，陈胜勇 . 2021 . 结合空间深度卷积和残差的大尺度点云场景分割 . 中国图象图形学报， 26 （ 12 ）： 2848 - 2859 ［ DOI： 10.11834/jig.200477 http://dx.doi.org/10.11834/jig.200477 ］

Liu X P ， Ma Y X ， Xu K ， Wan J W and Guo Y L . 2022 . Multi-scale transformer based point cloud completion network . Journal of Image and Graphics ， 27 （ 2 ）： 538 - 549

刘心溥，马燕新，许可，万建伟，郭裕兰 . 2022 . 嵌入Transformer结构的多尺度点云补全 . 中国图象图形学报， 27 （ 2 ）： 538 - 549 ［ DOI： 10.11834/jig.210510 http://dx.doi.org/10.11834/jig.210510 ］

Liu Y C ， Fan B ， Xiang S M and Pan C H . 2019b . Relation-shape convolutional neural network for point cloud analysis // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Long Beach， USA ： IEEE： 8887 - 8896 ［ DOI： 10.1109/CVPR.2019.00910 http://dx.doi.org/10.1109/CVPR.2019.00910 ］

Liu Z ， Hu H ， Cao Y ， Zhang Z and Tong X . 2020 . A closer look at local aggregation operators in point cloud analysis // Proceedings of the 16th European Conference on Computer Vision . Glasgow， UK ： Springer： 326 - 342 ［ DOI： 10.1007/978-3-030-58592-1_20 http://dx.doi.org/10.1007/978-3-030-58592-1_20 ］

Liu Z J ， Tang H T ， Lin Y J and Han S . 2019c . Point-voxel CNN for efficient 3D deep learning // Proceedings of the 33rd International Conference on Neural Information Processing Systems . Vancouver， Canada ： Curran Associates Inc.： #87

Long J ， Shelhamer E and Darrell T . 2015 . Fully convolutional networks for semantic segmentation // Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition . Boston， USA ： IEEE： 3431 - 3440 ［ DOI： 10.1109/CVPR.2015.7298965 http://dx.doi.org/10.1109/CVPR.2015.7298965 ］

Long X X ， Cheng X J ， Zhu H ， Zhang P J ， Liu H M ， Li J ， Zheng L T ， Hu Q Y ， Liu H ， Cao X ， Yang R G ， Wu Y H ， Zhang G F ， Liu Y B ， Xu K ， Guo Y L and Chen B Q . 2021 . Recent progress in 3D vision . Journal of Image and Graphics ， 26 （ 6 ）： 1389 - 1428

龙霄潇，程新景，朱昊，张朋举，刘浩敏，李俊，郑林涛，胡庆拥，刘浩，曹汛，杨睿刚，吴毅红，章国锋，刘烨斌，徐凯，郭裕兰，陈宝权 . 2021 . 3维视觉前沿进展 . 中国图象图形学报， 26 （ 6 ）： 1389 - 1428 ［ DOI： 10.11834/jig.210043 http://dx.doi.org/10.11834/jig.210043 ］

Ma Y X ， Wang T ， Bai X Y ， Yang H T ， Hou Y N ， Wang Y M ， Qiao Y ， Yang R G ， Manocha D and Zhu X G . 2022 . Vision-centric BEV perception： a survey ［EB/OL］. ［ 2022-08-04 ］. https://arxiv.org/pdf/2208.02797.pdf https://arxiv.org/pdf/2208.02797.pdf

Masci J ， Boscaini D ， Bronstein M M and Vandergheynst P . 2015 . Geodesic convolutional neural networks on riemannian manifolds // Proceedings of 2015 IEEE International Conference on Computer Vision Workshops . Santiago， Chile ： IEEE： 832 - 840 ［ DOI： 10.1109/ICCVW.2015.112 http://dx.doi.org/10.1109/ICCVW.2015.112 ］

Mendoza A ， Apaza A ， Sipiran I and López C . 2020 . Refinement of predicted missing parts enhance point cloud completion ［EB/OL］. ［ 2023-01-03 ］. https://arxiv.org/pdf/2010.04278.pdf https://arxiv.org/pdf/2010.04278.pdf

Miao Y W ， Zhang L ， Liu J Z ， Wang J R and Liu F C . 2021 . An end-to-end shape-preserving point completion network . IEEE Computer Graphics and Applications ， 41 （ 3 ）： 20 - 33 ［ DOI： 10.1109/MCG.2021.3065533 http://dx.doi.org/10.1109/MCG.2021.3065533 ］

Milioto A ， Vizzo I ， Behley J and Stachniss C . 2019 . RangeNet ++： fast and accurate LiDAR semantic segmentation // Proceedings of 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems . Macau， China ： IEEE： 4213 - 4220 ［ DOI： 10.1109/IROS40897.2019.8967762 http://dx.doi.org/10.1109/IROS40897.2019.8967762 ］

Monti F ， Boscaini D ， Masci J ， Rodolà E ， Svoboda J and Bronstein M M . 2017 . Geometric deep learning on graphs and manifolds using mixture model CNNs // Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition . Honolulu， USA ： IEEE： 5425 - 5434 ［ DOI： 10.1109/CVPR.2017.576 http://dx.doi.org/10.1109/CVPR.2017.576 ］

Pan L ， Chen X Y ， Cai Z G ， Zhang J Z ， Zhao H Y ， Yi S and Liu Z W . 2021 . Variational relational point completion network // Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Nashville， USA ： IEEE： 8520 - 8529 ［ DOI： 10.1109/CVPR46437.2021.00842 http://dx.doi.org/10.1109/CVPR46437.2021.00842 ］

Park C ， Jeong Y ， Cho M and Park J . 2022 . Fast point transformer // Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition . New Orleans， USA ： IEEE： 16928 - 16937 ［ DOI： 10.1109/CVPR52688.2022.01644 http://dx.doi.org/10.1109/CVPR52688.2022.01644 ］

Peng Y J ， Chang M ， Wang Q ， Qian Y L ， Zhang Y K ， Wei M Q and Liu X Y . 2020 . Sparse-to-dense multi-encoder shape completion of unstructured point cloud . IEEE Access ， 8 ： 30969 - 30978 ［ DOI： 10.1109/ACCESS.2020.2973003 http://dx.doi.org/10.1109/ACCESS.2020.2973003 ］

Qi C R ， Su H ， Mo K and Guibas L J . 2017a . PointNet： deep learning on point sets for 3D classification and segmentation // Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition . Honolulu， USA ： IEEE： 77 - 85 ［ DOI： 10.1109/CVPR.2017.16 http://dx.doi.org/10.1109/CVPR.2017.16 ］

Qi C R ， Yi L ， Su H and Guibas L J . 2017b . PointNet++： deep hierarchical feature learning on point sets in a metric space // Proceedings of the 31st International Conference on Neural Information Processing Systems . Long Beach， USA ： Curran Associates Inc.： 5105 - 5114

Qin Z ， Yu H ， Wang C J ， Guo Y L ， Peng Y X and Xu K . 2022 . Geometric transformer for fast and robust point cloud registration // Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition . New Orleans， USA ： IEEE： 11133 - 11142 ［ DOI： 10.1109/CVPR52688.2022.01086 http://dx.doi.org/10.1109/CVPR52688.2022.01086 ］

Ravanbakhsh S ， Schneider J G and Póczos B . 2017 . Deep learning with sets and point clouds // Proceedings of the 5th International Conference on Learning Representations . Toulon， France ： OpenReview.net： 1 - 12

Rusu R B ， Blodow N and Beetz M . 2009 . Fast point feature histograms （FPFH） for 3D registration // Proceedings of 2009 IEEE International Conference on Robotics and Automation . Kobe， Japan ： IEEE： 3212 - 3217 ［ DOI： 10.1109/ROBOT.2009.5152473 http://dx.doi.org/10.1109/ROBOT.2009.5152473 ］

Rusu R B ， Blodow N ， Marton Z S and Beetz M . 2008 . Aligning point cloud views using persistent feature histograms // Proceedings of 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems . Nice， France ： IEEE： 3384 - 3391 ［ DOI： 10.1109/IROS.2008.4650967 http://dx.doi.org/10.1109/IROS.2008.4650967 ］

Salti S ， Tombari F and Di Stefano L . 2014 . SHOT： unique signatures of histograms for surface and texture description . Computer Vision and Image Understanding ， 125 ： 251 - 264 ［ DOI： 10.1016/j.cviu.2014.04.011 http://dx.doi.org/10.1016/j.cviu.2014.04.011 ］

Sarlin P E ， DeTone D ， Malisiewicz T and Rabinovich A . 2020 . SuperGlue： learning feature matching with graph neural networks // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Seattle， USA ： IEEE： 4937 - 4946 ［ DOI： 10.1109/CVPR42600.2020.00499 http://dx.doi.org/10.1109/CVPR42600.2020.00499 ］

Sautier C ， Puy G ， Gidaris S ， Boulch A ， Bursuc A and Marlet R . 2022 . Image-to-lidar self-supervised distillation for autonomous driving data // Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition . New Orleans， USA ： IEEE： 9891 - 9901 ［ DOI： 10.1109/CVPR52688.2022.00966 http://dx.doi.org/10.1109/CVPR52688.2022.00966 ］

Schutt P ， Rosu R A and Behnke S . 2022 . Abstract flow for temporal semantic segmentation on the permutohedral lattice // Proceedings of 2022 International Conference on Robotics and Automation . Philadelphia， USA ： IEEE： 5139 - 5145 ［ DOI： 10.1109/ICRA46639.2022.9811818 http://dx.doi.org/10.1109/ICRA46639.2022.9811818 ］

Silberman N ， Hoiem D ， Kohli P and Fergus R . 2012 . Indoor segmentation and support inference from RGBD images // Proceedings of the 12th European Conference on Computer Vision . Florence， Italy ： Springer： 746 - 760 ［ DOI： 10.1007/978-3-642-33715-4_54 http://dx.doi.org/10.1007/978-3-642-33715-4_54 ］

Simonovsky M and Komodakis N . 2017 . Dynamic edge-conditioned filters in convolutional neural networks on graphs // Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition . Honolulu， USA ： IEEE： 29 - 38 ［ DOI： 10.1109/CVPR.2017.11 http://dx.doi.org/10.1109/CVPR.2017.11 ］

Song S R ， Lichtenberg S P and Xiao J X . 2015 . SUN RGB-D： a RGB-D scene understanding benchmark suite // Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition . Boston， USA ： IEEE： 567 - 576 ［ DOI： 10.1109/CVPR.2015.7298655 http://dx.doi.org/10.1109/CVPR.2015.7298655 ］

Spurek P ， Kasymov A ， Mazur M ， Janik D ， Tadeja S ， Struski Ł ， Tabor J and Trzciński T . 2021 . HyperPocket： generative point cloud completion // Proceedings of 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems . Kyoto， Japan ： IEEE： 6848 - 6853 ［ DOI： 10.1109/IROS47612.2022.9981829 http://dx.doi.org/10.1109/IROS47612.2022.9981829 ］

Sun J M ， Shen Z H ， Wang Y ， Bao H J and Zhou X W . 2021 . LoFTR： detector-free local feature matching with transformers // Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Nashville， USA ： IEEE： 8918 - 8927 ［ DOI： 10.1109/CVPR46437.2021.00881 http://dx.doi.org/10.1109/CVPR46437.2021.00881 ］

Sun P ， Kretzschmar H ， Dotiwalla X ， Chouard A ， Patnaik V ， Tsui P ， Guo J ， Zhou Y ， Chai Y ， Caine B ， Vasudevan V ， Han W ， Ngiam J ， Zhao H ， Timofeev A ， Ettinger S ， Krivokon M ， Gao A ， Joshi A ， Zhang Y ， Shlens J ， Chen Z F and Anguelov D . 2020 . Scalability in perception for autonomous driving： waymo open dataset // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Seattle， USA ： IEEE： 2443 - 2451 ［ DOI： 10.1109/CVPR42600.2020.00252 http://dx.doi.org/10.1109/CVPR42600.2020.00252 ］

Tan X ， Lin J Y ， Xu K ， Chen P ， Ma L Z and Lau R W H . 2023 . Mirror detection with the visual chirality cue . IEEE Transactions on Pattern Analysis and Machine Intelligence ， 45 （ 3 ）： 3492 - 3504 ［ DOI： 10.1109/TPAMI.2022.3181030 http://dx.doi.org/10.1109/TPAMI.2022.3181030 ］

Tan X ， Xu K ， Cao Y ， Zhang Y H ， Ma L Z and Lau R W H . 2021 . Night-time scene parsing with a large real dataset . IEEE Transactions on Image Processing ， 30 ： 9085 - 9098 ［ DOI： 10.1109/TIP.2021.3122004 http://dx.doi.org/10.1109/TIP.2021.3122004 ］

Tang H T ， Liu Z J ， Zhao S Y ， Lin Y J ， Lin J ， Wang H R and Han S . 2020 . Searching efficient 3D architectures with sparse point-voxel convolution // Proceedings of the 16th European Conference on Computer Vision . Glasgow， UK ： Springer： 685 - 702 ［ DOI： 10.1007/978-3-030-58604-1_41 http://dx.doi.org/10.1007/978-3-030-58604-1_41 ］

Tang J S ， Gong Z J ， Yi R ， Xie Y and Ma L Z . 2022 . LAKe-Net： topology-aware point cloud completion by localizing aligned keypoints // Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition . New Orleans， USA ： 1716 - 1725 ［ DOI： 10.1109/cvpr52688.2022.00177 http://dx.doi.org/10.1109/cvpr52688.2022.00177 ］

Tarvainen A and Valpola H . 2017 . Mean teachers are better role models： weight-averaged consistency targets improve semi-supervised deep learning results // Proceedings of the 31st International Conference on Neural Information Processing Systems . Long Beach，USA ： Curran Associates Inc.： 1195 - 1204

Te G S ， Hu W ， Zheng A M and Guo Z M . 2018 . RGCNN： regularized graph CNN for point cloud segmentation // Proceedings of the 26th ACM International Conference on Multimedia . Seoul， Korea （South）： ACM： 746 - 754 ［ DOI： 10.1145/3240508.3240621 http://dx.doi.org/10.1145/3240508.3240621 ］

Thomas H ， Qi C R ， Deschaud J E ， Marcotegui B ， Goulette F and Guibas L . 2019 . KPConv： flexible and deformable convolution for point clouds // Proceedings of 2019 IEEE/CVF International Conference on Computer Vision . Seoul， Korea （South）： IEEE： 6410 - 6419 ［ DOI： 10.1109/ICCV.2019.00651 http://dx.doi.org/10.1109/ICCV.2019.00651 ］

Tombari F ， Salti S and Di Stefano L . 2010 . Unique shape context for 3D data description // Proceedings of 2010 ACM Workshop on 3D Object Retrieval . Firenze， Italy ： ACM： 57 - 62 ［ DOI： 10.1145/1877808.1877821 http://dx.doi.org/10.1145/1877808.1877821 ］

Unal O ， Dai D X and Van Gool L . 2022 . Scribble-supervised LiDAR semantic segmentation // Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition . New Orleans， USA ： IEEE： 2687 - 2697 ［ DOI： 10.1109/CVPR52688.2022.00272 http://dx.doi.org/10.1109/CVPR52688.2022.00272 ］

Vaswani A ， Shazeer N ， Parmar N ， Uszkoreit J ， Jones L ， Gomez A N ， Kaiser Ł and Polosukhin I . 2017 . Attention is all you need // Proceedings of the 31st International Conference on Neural Information Processing Systems . Long Beach， USA ： Curran Associates Inc.： 6000 - 6010

Vu T ， Kim K ， Luu T M ， Nguyen T and Yoo C D . 2022 . SoftGroup for 3D instance segmentation on point clouds // Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition . New Orleans， USA ： IEEE： 2698 - 2707 ［ DOI： 10.1109/CVPR52688.2022.00273 http://dx.doi.org/10.1109/CVPR52688.2022.00273 ］

Wang C ， Samari B and Siddiqi K . 2018a . Local spectral graph convolution for point set feature learning // Proceedings of the 15th European Conference on Computer Vision . Munich， Germany ： Springer： 56 - 71 ［ DOI： 10.1007/978-3-030-01225-0_4 http://dx.doi.org/10.1007/978-3-030-01225-0_4 ］

Wang W Y ， Huang Q G ， You S Y ， Yang C and Neumann U . 2017 . Shape inpainting using 3D generative adversarial network and recurrent convolutional networks // Proceedings of 2017 IEEE International Conference on Computer Vision . Venice， Italy ： IEEE： 2317 - 2325 ［ DOI： 10.1109/ICCV.2017.252 http://dx.doi.org/10.1109/ICCV.2017.252 ］

Wang W Y ， Yu R ， Huang Q G and Neumann U . 2018b . SGPN： similarity group proposal network for 3D point cloud instance segmentation // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City， USA ： IEEE： 2569 - 2578 ［ DOI： 10.1109/CVPR.2018.00272 http://dx.doi.org/10.1109/CVPR.2018.00272 ］

Wang X G ， Ang M H and Lee G H . 2020a . Cascaded refinement network for point cloud completion // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Seattle， USA ： IEEE： 787 - 796 ［ DOI： 10.1109/cvpr42600.2020.00087 http://dx.doi.org/10.1109/cvpr42600.2020.00087 ］

Wang X G ， Ang M H and Lee G H . 2020b . Point cloud completion by learning shape priors // Proceedings of 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems . Las Vegas， USA ： IEEE： 10719 - 10726 ［ DOI： 10.1109/IROS45743.2020.9340862 http://dx.doi.org/10.1109/IROS45743.2020.9340862 ］

Wang Y ， Sun Y B ， Liu Z W ， Sarma S E ， Bronstein M M and Solomon J M . 2019 . Dynamic graph CNN for learning on point clouds . ACM Transactions on Graphics ， 38 （ 5 ）： # 146 ［ DOI： 10.1145/3326362 http://dx.doi.org/10.1145/3326362 ］

Wen X ， Han Z Z ， Cao Y P ， Wan P F ， Zheng W and Liu Y S . 2021 . Cycle4Completion： unpaired point cloud completion using cycle transformation with missing region coding // Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Nashville， USA ： IEEE： 13075 - 13084 ［ DOI： 10.1109/CVPR46437.2021.01288 http://dx.doi.org/10.1109/CVPR46437.2021.01288 ］

Wu B C ， Zhou X Y ， Zhao S C ， Yue X Y and Keutzer K . 2019a . SqueezeSegV2： improved model structure and unsupervised domain adaptation for road-object segmentation from a LiDAR point cloud // Proceedings of 2019 International Conference on Robotics and Automation . Montreal， Canada ： IEEE： 4376 - 4382 ［ DOI： 10.1109/ICRA.2019.8793495 http://dx.doi.org/10.1109/ICRA.2019.8793495 ］

Wu H and Miao Y B . 2021a . Cross-regional attention network for point cloud completion // Proceedings of the 25th International Conference on Pattern Recognition . Milan， Italy ： IEEE： 10274 - 10280 ［ DOI： 10.1109/ICPR48806.2021.9413104 http://dx.doi.org/10.1109/ICPR48806.2021.9413104 ］

Wu H ， Miao Y B and Fu R C . 2021b . Point cloud completion using multiscale feature fusion and cross-regional attention . Image and Vision Computing ， 111 ： # 104193 ［ DOI： 10.1016/J.IMAVIS.2021.104193 http://dx.doi.org/10.1016/J.IMAVIS.2021.104193 ］

Wu W X ， Qi Z G and Li F X . 2019b . PointConv： deep convolutional networks on 3D point clouds // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Long Beach， USA ： IEEE： 9613 - 9622 ［ DOI： 10.1109/CVPR.2019.00985 http://dx.doi.org/10.1109/CVPR.2019.00985 ］

Wu X Y ， Lao Y X ， Jiang L ， Liu X H and Zhao H S . 2022 . Point transformer V2： grouped vector attention and partition-based pooling // Proceedings of the 36th Conference on Neural Information Processing Systems . New Orleans， USA ： Curran Associates Inc.

Wu Z R ， Song S R ， Khosla A ， Yu F ， Zhang L G ， Tang X O and Xiao J X . 2015 . 3D ShapeNets： a deep representation for volumetric shapes // Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition . Boston， USA ： IEEE： 1912 - 1920 ［ DOI： 10.1109/CVPR.2015.7298801 http://dx.doi.org/10.1109/CVPR.2015.7298801 ］

Xia Y ， Liu W ， Luo Z ， Xu Y and Stilla U . 2020 . Completion of sparse and partial point clouds of vehicles using a novel end-to-end network // Proceedings of 2020 ISPRS Annals of the Photogrammetry， Remote Sensing and Spatial Information Sciences . ［s.l.］： ISPRS： 933 - 940 ［ DOI： 10.5194/isprs-annals-v-2-2020-933-2020 http://dx.doi.org/10.5194/isprs-annals-v-2-2020-933-2020 ］

Xia Y Q ， Xia Y ， Li W ， Song R ， Cao K L and Stilla U . 2021 . ASFM-Net： asymmetrical Siamese feature matching network for point completion // Proceedings of the 29th ACM International Conference on Multimedia . Chengdu， China ： ACM： 1938 - 1947 ［ DOI： 10.1145/3474085.3475348 http://dx.doi.org/10.1145/3474085.3475348 ］

Xu J Y ， Tang X ， Zhu Y S ， Sun J and Pu S L . 2021a . SGMNet： learning rotation-invariant point cloud representations via sorted Gram matrix // Proceedings of 2021 IEEE/CVF International Conference on Computer Vision . Montreal， Canada ： IEEE： 10448 - 10457 ［ DOI： 10.1109/ICCV48922.2021.01030 http://dx.doi.org/10.1109/ICCV48922.2021.01030 ］

Xu J Y ， Zhang R X ， Dou J ， Zhu Y S ， Sun J and Pu S L . 2021b . RPVNet： a deep and efficient range-point-voxel fusion network for LiDAR point cloud segmentation // Proceedings of 2021 IEEE/CVF International Conference on Computer Vision . Montreal， Canada ： IEEE： 16004 - 16013 ［ DOI： 10.1109/ICCV48922.2021.01572 http://dx.doi.org/10.1109/ICCV48922.2021.01572 ］

Xu X and Lee G H . 2020 . Weakly supervised semantic point cloud segmentation： towards 10×fewer labels // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Seattle， USA ： IEEE： 13703 - 13712 ［ DOI： 10.1109/CVPR42600.2020.01372 http://dx.doi.org/10.1109/CVPR42600.2020.01372 ］

Xu Y F ， Fan T Q ， Xu M Y ， Zeng L and Qiao Y . 2018 . SpiderCNN： deep learning on point sets with parameterized convolutional filters // Proceedings of the 15th European Conference on Computer Vision . Munich， Germany ： Springer： 90 - 105 ［ DOI： 10.1007/978-3-030-01237-3_6 http://dx.doi.org/10.1007/978-3-030-01237-3_6 ］

Yan X ， Zheng C D ， Li Z ， Wang S and Cui S G . 2020 . PointASNL： robust point clouds processing using nonlocal neural networks with adaptive sampling // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Seattle， USA ： IEEE： 5588 - 5597 ［ DOI： 10.1109/CVPR42600.2020.00563 http://dx.doi.org/10.1109/CVPR42600.2020.00563 ］

Yang B ， Wang J N ， Clark R ， Hu Q Y ， Wang S ， Markham A and Trigoni N . 2019a . Learning object bounding boxes for 3D instance segmentation on point clouds // Proceedings of the 33rd International Conference on Neural Information Processing Systems . Vancouver， Canada ： Curran Associates Inc.： #650

Yang J C ， Zhang Q ， Ni B B ， Li L G ， Liu J X ， Zhou M D and Tian Q . 2019b . Modeling point clouds with self-attention and gumbel subset sampling // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Long Beach， USA ： IEEE： 3318 - 3327 ［ DOI： 10.1109/CVPR.2019.00344 http://dx.doi.org/10.1109/CVPR.2019.00344 ］

Ye M S ， Wan R ， Xu S J ， Cao T Y and Chen Q F . 2022 . Efficient point cloud segmentation with geometry-aware sparse networks // Proceedings of the 17th European Conference on Computer Vision . Tel Aviv， Israel ： Springer： 196 - 212 ［ DOI： 10.1007/978-3-031-19842-7_12 http://dx.doi.org/10.1007/978-3-031-19842-7_12 ］

Ye M S ， Xu S J ， Cao T Y and Chen Q F . 2021 . DRINet： a dual-representation iterative learning network for point cloud segmentation // Proceedings of 2021 IEEE/CVF International Conference on Computer Vision . Montreal， Canada ： IEEE： 7427 - 7436 ［ DOI： 10.1109/ICCV48922.2021.00735 http://dx.doi.org/10.1109/ICCV48922.2021.00735 ］

Yew Z J and Lee G H . 2022 . REGTR： end-to-end point cloud correspondences with transformers // Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition . New Orleans， USA ： IEEE： 6667 - 6676 ［ DOI： 10.1109/CVPR52688.2022.00656 http://dx.doi.org/10.1109/CVPR52688.2022.00656 ］

Yi L ， Su H ， Guo X W and Guibas L . 2017 . SyncSpecCNN： synchronized spectral CNN for 3D shape segmentation // Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition . Honolulu， USA ： IEEE： 6584 - 6592 ［ DOI： 10.1109/CVPR.2017.697 http://dx.doi.org/10.1109/CVPR.2017.697 ］

Yi L ， Zhao W ， Wang H ， Sung M and Guibas L J . 2019 . GSPN： generative shape proposal network for 3D instance segmentation in point cloud // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Long Beach， USA ： IEEE： 3942 - 3951 ［ DOI： 10.1109/CVPR.2019.00407 http://dx.doi.org/10.1109/CVPR.2019.00407 ］

You Y ， Lou Y J ， Liu Q ， Tai Y W ， Ma L Z ， Lu C W and Wang W M . 2020 . Pointwise rotation-invariant network with adaptive sampling and 3D spherical voxel convolution // Proceedings of the 34th AAAI Conference on Artificial Intelligence . New York， USA ： AAAI Press： 12717 - 12724 ［ DOI： 10.1609/aaai.v34i07.6965 http://dx.doi.org/10.1609/aaai.v34i07.6965 ］

You Y ， Lou Y J ， Shi R X ， Liu Q ， Tai Y W ， Ma L Z ， Wang W M and Lu C W . 2022 . PRIN/SPRIN： on extracting point-wise rotation invariant features . IEEE Transactions on Pattern Analysis and Machine Intelligence ， 44 （ 12 ）： 9489 - 9502 ［ DOI： 10.1109/TPAMI.2021.3130590 http://dx.doi.org/10.1109/TPAMI.2021.3130590 ］

Yu H ， Li F ， Saleh M ， Busam B and Ilic S . 2021a . CoFiNet： reliable coarse-to-fine correspondences for robust pointcloud registration //Proceedings of the 35th Advances in Neural Information Processing Systems. ［s.l.］： Curran Associates， Inc.： 23872 - 23884

Yu R X ， Wei X ， Tombari F and Sun J . 2020 . Deep positional and relational feature learning for rotation-invariant point cloud analysis // Proceedings of the 16th European Conference on Computer Vision . Glasgow， UK ： Springer： 217 - 233 ［ DOI： 10.1007/978-3-030-58607-2_13 http://dx.doi.org/10.1007/978-3-030-58607-2_13 ］

Yu X M ， Rao Y M ， Wang Z Y ， Liu Z Y ， Lu J W and Zhou J . 2021b . PoinTr： diverse point cloud completion with geometry-aware transformers // Proceedings of 2021 IEEE/CVF International Conference on Computer Vision . Montreal， Canada ： IEEE： 12478 - 12487 ［ DOI： 10.1109/ICCV48922.2021.01227 http://dx.doi.org/10.1109/ICCV48922.2021.01227 ］

Yu X M ， Tang L L ， Rao Y M ， Huang T J ， Zhou J and Lu J W . 2022 . Point-BERT： pre-training 3D point cloud transformers with masked point modeling // Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition . New Orleans， USA ： IEEE： 19291 - 19300 ［ DOI： 10.1109/CVPR52688.2022.01871 http://dx.doi.org/10.1109/CVPR52688.2022.01871 ］

Zaheer M ， Kottur S ， Ravanbhakhsh S ， Póczos B ， Salakhutdinov R and Smola A J . 2017 . Deep sets // Proceedings of the 31st International Conference on Neural Information Processing Systems . Long Beach， USA ： Curran Associates Inc.： 3394 - 3404

Zamorski M ， Zięba M and Świątek J . 2020 . Generative modeling in application to point cloud completion // Proceedings of the 19th International Conference on Artificial Intelligence and Soft Computing . Zakopane， Poland ： Springer： 292 - 302 ［ DOI： 10.1007/978-3-030-61401-0_28 http://dx.doi.org/10.1007/978-3-030-61401-0_28 ］

Zeng A ， Song S R ， Nießner M ， Fisher M ， Xiao J X and Funkhouser T . 2017 . 3DMatch： learning local geometric descriptors from RGB-d reconstructions // Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition . Honolulu， USA ： IEEE： 199 - 208 ［ DOI： 10.1109/CVPR.2017.29 http://dx.doi.org/10.1109/CVPR.2017.29 ］

Zhang C ， Wan H C ， Shen X Y and Wu Z Z . 2022 . PatchFormer： an efficient point transformer with patch attention // Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition . New Orleans， USA ： IEEE： 11789 - 11798 ［ DOI： 10.1109/CVPR52688.2022.01150 http://dx.doi.org/10.1109/CVPR52688.2022.01150 ］

Zhang J M ， Yu M Y ， Vasudevan R and Johnson-Roberson M . 2020a . Learning rotation-invariant representations of point clouds using aligned edge convolutional neural networks // Proceedings of 2020 International Conference on 3D Vision （3DV） . Fukuoka， Japan ： IEEE： 200 - 209 ［ DOI： 10.1109/3DV50981.2020.00030 http://dx.doi.org/10.1109/3DV50981.2020.00030 ］

Zhang J Z ， Chen X Y ， Cai Z G ， Pan L ， Zhao H Y ， Yi S ， Yeo C K ， Dai B and Loy C C . 2021a . Unsupervised 3D shape completion through GAN inversion // Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Nashville， USA ： IEEE： 1768 - 1777 ［ DOI： 10.1109/CVPR46437.2021.00181 http://dx.doi.org/10.1109/CVPR46437.2021.00181 ］

Zhang W X ， Yan Q G and Xiao C X . 2020b . Detail preserved point cloud completion via separated feature aggregation // Proceedings of the 16th European Conference on Computer Vision . Glasgow， UK ： Springer： 512 - 528 ［ DOI： 10.1007/978-3-030-58595-2_31 http://dx.doi.org/10.1007/978-3-030-58595-2_31 ］

Zhang Y ， Zhou Z X ， David P ， Yue X Y ， Xi Z R ， Gong B Q and Foroosh H . 2020c . PolarNet： an improved grid representation for online LiDAR point clouds semantic segmentation // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Seattle， USA ： IEEE： 9598 - 9607 ［ DOI： 10.1109/CVPR42600.2020.00962 http://dx.doi.org/10.1109/CVPR42600.2020.00962 ］

Zhang Y C ， Qu Y Y ， Xie Y ， Li Z H ， Zheng S S and Li C H . 2021b . Perturbed self-distillation： weakly supervised large-scale point cloud semantic segmentation // Proceedings of 2021 IEEE/CVF International Conference on Computer Vision . Montreal， Canada ： IEEE： 15500 - 15508 ［ DOI： 10.1109/ICCV48922.2021.01523 http://dx.doi.org/10.1109/ICCV48922.2021.01523 ］

Zhang Y N ， Huang D and Wang Y H . 2021c . PC-RGNN： point cloud completion and graph neural network for 3D object detection // Proceedings of the 35th AAAI Conference on Artificial Intelligence . Palo Alto， USA ： AAAI： 3430 - 3437 ［ DOI： 10.1609/AAAI.v35i4.16456 http://dx.doi.org/10.1609/AAAI.v35i4.16456 ］

Zhang Z Y ， Hua B S ， Rosen D W and Yeung S K . 2019 . Rotation invariant convolutions for 3D point clouds deep learning // Proceedings of 2019 International Conference on 3D Vision （3DV） . Quebec City， Canada ： IEEE： 204 - 213 ［ DOI： 10.1109/3DV.2019.00031 http://dx.doi.org/10.1109/3DV.2019.00031 ］

Zhao C ， Yang J Q ， Xiong X ， Zhu A F ， Cao Z G and Li X . 2022a . Rotation invariant point cloud analysis： where local geometry meets global topology . Pattern Recognition ， 127 ： # 108626 ［ DOI： 10.1016/j.patcog.2022.108626 http://dx.doi.org/10.1016/j.patcog.2022.108626 ］

Zhao H S ， Jia J Y and Koltun V . 2020 . Exploring self-attention for image recognition // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Seattle， USA ： IEEE： 10073 - 10082 ［ DOI： 10.1109/CVPR42600.2020.01009 http://dx.doi.org/10.1109/CVPR42600.2020.01009 ］

Zhao H S ， Jiang L ， Fu C W and Jia J Y . 2019 . PointWeb： enhancing local neighborhood features for point cloud processing // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Long Beach， USA ： IEEE： 5560 - 5568 ［ DOI： 10.1109/CVPR.2019.00571 http://dx.doi.org/10.1109/CVPR.2019.00571 ］

Zhao H S ， Jiang L ， Jia J Y ， Torr P and Koltun V . 2021 . Point transformer // Proceedings of 2021 IEEE/CVF International Conference on Computer Vision . Montreal， Canada ： IEEE： 16239 - 16248 ［ DOI： 10.1109/ICCV48922.2021.01595 http://dx.doi.org/10.1109/ICCV48922.2021.01595 ］

Zhao X ， Zhang B W ， Wu J J ， Hu R Z and Komura T . 2022b . Relationship-based point cloud completion . IEEE Transactions on Visualization and Computer Graphics ， 28 （ 12 ）： 4940 - 4950 ［ DOI： 10.1109/TVCG.2021.3109392 http://dx.doi.org/10.1109/TVCG.2021.3109392 ］

Zhou T H ， Chen J N ， Shi Y N ， Jiang K ， Yang M M and Yang D G . 2022 . Bridging the view disparity between radar and camera features for multi-modal fusion 3D object detection . IEEE Transactions on Intelligent Vehicles ， 8 （ 2 ）： 1523 - 1535 ［ DOI： 10.1109/TIV.2023.3240287 http://dx.doi.org/10.1109/TIV.2023.3240287 ］

Zhu X G ， Zhou H ， Wang T ， Hong F Z ， Ma Y X ， Li W ， Li H S and Lin D H . 2021 . Cylindrical and asymmetrical 3D convolution networks for LiDAR segmentation // Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Nashville， USA ： IEEE： 9934 - 9943 ［ DOI： 10.1109/cvpr46437.2021.00981 http://dx.doi.org/10.1109/cvpr46437.2021.00981 ］

Zhuang Z W ， Li R ， Jia K ， Wang Q C ， Li Y Q and Tan M K . 2021 . Perception-aware multi-sensor fusion for 3D LiDAR semantic segmentation // Proceedings of 2021 IEEE/CVF International Conference on Computer Vision . Montreal， Canada ： IEEE： 16260 - 16270 ［ DOI： 10.1109/ICCV48922.2021.01597 http://dx.doi.org/10.1109/ICCV48922.2021.01597 ］

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