基于可变形图卷积的点云表征学习

计算机科学 ›› 2022, Vol. 49 ›› Issue (8): 273-278.doi: 10.11896/jsjkx.210900023

基于可变形图卷积的点云表征学习

李宗民¹, 张玉鹏¹, 刘玉杰¹, 李华²

1 中国石油大学(华东)计算机科学与技术学院山东青岛 266580
2 中国科学院计算技术研究所北京 100190

收稿日期:2021-09-03 修回日期:2022-03-24 发布日期:2022-08-02
通讯作者: 张玉鹏(S19070005@s.upc.edu.cn)
作者简介:(lizongmin@upc.edu.cn)
基金资助:
国家重点研发计划(2019YFF0301800);国家自然科学基金(61379106);山东省自然科学基金(ZR2013FM036,ZR2015FM011)

Deformable Graph Convolutional Networks Based Point Cloud Representation Learning

LI Zong-min¹, ZHANG Yu-peng¹, LIU Yu-jie¹, LI Hua²

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).

摘要/Abstract

摘要： 虽然深层神经网络较为成功地解决了点云数据稀疏和不规则等问题,但是点云局部特征的学习仍然是一个非常具有挑战性的问题。现有的用于点云表征学习的网络存在点与点之间相互独立提取特征的问题,为此提出了一种全新的空域图卷积。首先,在构造图结构时提出了自适应空洞K近邻算法,以最大程度地捕获局部拓扑结构信息;其次,在卷积中加入了卷积核每条边与感受野图之间的角度特征,保证了更具鉴别力的特征提取;最后,为了充分利用局部特征,提出了一种全新的图金字塔池化,以更好地融合多尺度特征。在标准公开数据集ModelNet40和ShapeNet上测试该算法,分别取得了93.2%与86.5%的准确度。实验结果表明,该算法在点云表征学习中处于领先水平。

关键词: 表征学习, 点云, 局部特征, 图卷积神经网络

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

中图分类号:

TP391.41

李宗民, 张玉鹏, 刘玉杰, 李华. 基于可变形图卷积的点云表征学习[J]. 计算机科学, 2022, 49(8): 273-278. https://doi.org/10.11896/jsjkx.210900023

LI Zong-min, ZHANG Yu-peng, LIU Yu-jie, LI Hua. Deformable Graph Convolutional Networks Based Point Cloud Representation Learning[J]. Computer Science, 2022, 49(8): 273-278. https://doi.org/10.11896/jsjkx.210900023

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