Computer Science

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).

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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
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