Computer Science ›› 2022, Vol. 49 ›› Issue (8): 273-278.doi: 10.11896/jsjkx.210900023

• Artificial Intelligence • Previous Articles     Next Articles

Deformable Graph Convolutional Networks Based Point Cloud Representation Learning

LI Zong-min1, ZHANG Yu-peng1, LIU Yu-jie1, LI Hua 2   

  1. 1 College of Computer Science and Technology,China University of Petroleum,Qingdao,Shandong 266580,China
    2 Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China
  • Received:2021-09-03 Revised:2022-03-24 Published:2022-08-02
  • About author:LI Zong-min,born in 1965,Ph.D,professor,is a member of China Computer Federation.His main research interests include computer graphics,picture processing and scientific computing visuali-zation.
    ZHANG Yu-peng,born in 1997,postgraduate.His main research interests include point cloud representation learning,graph neural network and geometric invariance.
  • Supported by:
    National Key R & D Program(2019YFF0301800),National Natural Science Foundation of China(61379106)and Shandong Provincial Natural Science Foundation(ZR2013FM036,ZR2015FM011).

Abstract: Although the sparseness and irregularity of point cloud data have been successfully solved by deep neural networks.However,how to learn the local features of point clouds is still a challenging problem.Existing networks for point cloud representation learning have the problem of extracting features independently between points and points.To this end,a new spatial graph convolution is proposed.Firstly,an adaptive hole K-nearest neighbor algorithm is proposed when constructing the graph structure to maximize local topo-logical structure information.Secondly,the angle feature between each edge of the convolution kernel and the receptive field map is added to the convolution,which ensures more discriminative feature extraction.Finally,in order to make full use of local features,a novel graph pyramid pooling is proposed.This algorithm is tested on the standard public data sets ModelNet40 and ShapeNet,and the accuracy is 93.2% and 86.5% respectively.Experimental results show that the proposed algorithm is at a leading level in point cloud representation learning.

Key words: Graph neural convolutional networks, Local feature, Point clouds, Representation learning

CLC Number: 

  • TP391.41
[1]GUO Y,WANG H,HU Q,et al.Deep Learning for 3D Point Clouds:A Survey[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2020,43(12):4338-4364.
[2]QI C R,SU H,MO K,et al.Pointnet:Deep learning on point sets for 3d classification and segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:652-660.
[3]QI C R,YI L,SU H,et al.PointNet++:deep hierarchical feature learning on point sets in a metric space[EB/OL].
[4]LI Y,BU R,SUN M,et al.B:PointCNN:Con-volution on X-transformed points[C]//In Conference and Work-shop on Neural Information Processing Systems(NeurIPS).2018:820-830.
[5]LI R,LI X,HENG P A,et al.Pointaugment:an au-to-augmentation framework for point cloud classifica-tion[C]//Proceedings of the IEEE/CVF Conference on Com-puter Vision and Pattern Recognition.2020:6378-6387.
[6]JIE Z A,GC A,SH A,et al.Graph neural networks:A review of methods and applications[J].AI Open,2020,1:57-81.
[7]WANG Y,SUN Y,LIU Z,et al.Dynamic graph cnn for learning on point clouds[J].ACM Transactions On Graphics(tog),2019,38(5):1-12.
[8]LIN Z H,HUANG S Y,WANG Y C F.Convolution in the cloud:Learning deformable kernels in 3D graph convolution net-works for point cloud analysis[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:1800-1809.
[9]ZHAO H,JIANG L,FU C W,et al.PointWeb:Enhancing local neighborhood features for point cloud processing[C]//CVPR.2019:5565-5573.
[10]YANG J,ZHANG Q,NI B,et al.Modeling Point Clouds With Self-Attention and Gumbel Subset Sampling[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).IEEE,2019:3323-3332.
[11]CHOY C B,XU D F,GWAK J Y,et al.3D-R2N2:A Unified Approach for Single and Multi-view 3D Object Reconstruction[C]//Proceedings of the European Conference on Computer Vision(ECCV).2016:628-644.
[12]MATURANA D,SCHERER S.VoxNet:A 3D Convolutional Neural Network for Real-time Object Recognition[C]//Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS).IEEE,2015:922-928.
[13]MENG H Y,GAO L,LAI Y K,et al.VV-Net:Voxel VAE Net with Group Convolutions for Point Cloud Segmentation[J].ar-Xiv:1811.04337,2018.
[14]ROYNARD X,DESCHAUD J E,GOULETTE F.Classification of Point Cloud Scenes with Multiscale Voxel Deep Network[J].arXiv:1804.03583,2018.
[15]YAN Y,MAO Y,LI B.Second:Sparsely embedded convolu-tional detection[J/OL].Sensors,2018:18(10):3337.
[16]TE G,HU W,ZHENG A,et al.Rgcnn:Regularized graph cnn for point cloud segmentation[C]//Proceedings of the 26th ACM international conference on Multimedia.2018:746-754.
[17]ZHANG K,HAO M,WANG J,et al.Linked dynamic graphcnn:Learning on point cloud via linking hierarchical features[J].arXiv:1904.10014,2019.
[18]SIMONOVSKY M,KOMODAKIS N.Dynamic edge-conditioned filters in convolutional neural networks on graphs[C]//CVPR.2017:3693-3702.
[19]LI G,MÜLLER M,THABET A,et al.DeepGCNs:Can GCNs Go as Deep as CNNs? [C]//ICCV.2019:9267-9276.
[20]SHEN Y,FENG C,YANG Y,et al.Mining point cloud localstruc-tures by kernel correlation and graph pooling[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:4548-4557.
[21]XU Q,SUN X,WU C Y,et al.Grid-gcn for fast and scalable point cloud learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:5661-5670.
[22]HU H,WANG F,LE H.VA-GCN:A Vector Attention Graph Convolution Network for learning on Point Clouds[J/OL].
[23]HE K,ZHANG X,REN S,et al.Spatial pyramid pooling in deep convolutional networks for visual recognition[J].IEEETran-sactions on Pattern Analysis and Machine Intelligence,2015,37(9):1904-1916.
[24]SHILANE P,MIN P,KAZHDAN M,et al.The princeton shape benchmark[C]//Proceedings Shape Modeling Applications,2004.IEEE,2004:167-178.
[25]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(ToG),2016,35(6):1-12.
[26]KLOKOV R,LEMPITSKY V.Escape from cells:Deep kd-networks for the recognition of 3d point cloud models[C]//2017 IEEE International Conference on Computer Vision(ICCV).2017:863-872.
[27]HAN X F,HE Z Y,CHEN J,et al.Cross-Level Cross-ScaleCross-Attention Network for Point Cloud Representation[EB/OL].
[1] SONG Jie, LIANG Mei-yu, XUE Zhe, DU Jun-ping, KOU Fei-fei. Scientific Paper Heterogeneous Graph Node Representation Learning Method Based onUnsupervised Clustering Level [J]. Computer Science, 2022, 49(9): 64-69.
[2] HUANG Li, ZHU Yan, LI Chun-ping. Author’s Academic Behavior Prediction Based on Heterogeneous Network Representation Learning [J]. Computer Science, 2022, 49(9): 76-82.
[3] XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai. Temporal Knowledge Graph Representation Learning [J]. Computer Science, 2022, 49(9): 162-171.
[4] HUANG Pu, DU Xu-ran, SHEN Yang-yang, YANG Zhang-jing. Face Recognition Based on Locality Regularized Double Linear Reconstruction Representation [J]. Computer Science, 2022, 49(6A): 407-411.
[5] JIANG Zong-li, FAN Ke, ZHANG Jin-li. Generative Adversarial Network and Meta-path Based Heterogeneous Network Representation Learning [J]. Computer Science, 2022, 49(1): 133-139.
[6] WANG Ying-li, JIANG Cong-cong, FENG Xiao-nian, QIAN Tie-yun. Time Aware Point-of-interest Recommendation [J]. Computer Science, 2021, 48(9): 43-49.
[7] ZHAO Jin-long, ZHAO Zhong-ying. Recommendation Algorithm Based on Heterogeneous Information Network Embedding and Attention Neural Network [J]. Computer Science, 2021, 48(8): 72-79.
[8] YE Hong-liang, ZHU Wan-ning, HONG Lei. Music Style Transfer Method with Human Voice Based on CQT and Mel-spectrum [J]. Computer Science, 2021, 48(6A): 326-330.
[9] YANG Ru-han, DAI Yi-ru, WANG Jian, DONG Jin. Humans-Cyber-Physical Ontology Fusion of Industry Based on Representation Learning [J]. Computer Science, 2021, 48(5): 190-196.
[10] QIAN Sheng-sheng, ZHANG Tian-zhu, XU Chang-sheng. Survey of Multimedia Social Events Analysis [J]. Computer Science, 2021, 48(3): 97-112.
[11] WANG Xing , KANG Zhao. Smooth Representation-based Semi-supervised Classification [J]. Computer Science, 2021, 48(3): 124-129.
[12] LI Xin-chao, LI Pei-feng, ZHU Qiao-ming. Directed Network Representation Method Based on Hierarchical Structure Information [J]. Computer Science, 2021, 48(2): 100-104.
[13] WANG Xue-cen, ZHANG Yu, LIU Ying-jie, YU Ge. Evaluation of Quality of Interaction in Online Learning Based on Representation Learning [J]. Computer Science, 2021, 48(2): 207-211.
[14] FU Kun, ZHAO Xiao-meng, FU Zi-tong, GAO Jin-hui, MA Hao-ran. Deep Network Representation Learning Method on Incomplete Information Networks [J]. Computer Science, 2021, 48(12): 212-218.
[15] KANG Yan, KOU Yong-qi, XIE Si-yu, WANG Fei, ZHANG Lan, WU Zhi-wei, LI Hao. Deep Clustering Model Based on Fusion Variational Graph Attention Self-encoder [J]. Computer Science, 2021, 48(11A): 81-87.
Full text



No Suggested Reading articles found!