摘要
点云数据具有稀疏性、不规则性和置换不变性,缺乏拓扑信息,导致它的特征难以被提取,为此,提出一种孪生自适应图卷积算法(SAGCA)进行点云分类与分割。首先,构建特征关系图挖掘不规则、稀疏点云特征间的拓扑关系;其次,引入共享卷积学习权重的孪生构图思想,保证点云的置换不变性,使拓扑关系表达更准确;最后,采用整体、局部两种结合方式,将SAGCA与各种处理点云数据的深度学习网络相结合,增强网络的特征提取能力。分别在ScanObjectNN、ShapeNetPart和S3DIS数据集上进行分类、对象部件分割和场景语义分割实验的结果表明,相较于PointNet++基准网络,基于同样的数据集和评价标准,SAGCA分类实验的类别平均准确率(mAcc)提高了2.80个百分点,对象部件分割实验的总体类别平均交并比(IoU)提高了2.31个百分点,场景语义分割实验的类别平均交并比(mIoU)提高了2.40个百分点,说明SAGCA能有效增强网络的特征提取能力,适用于多种点云分类分割任务。
Point cloud data has sparsity,irregularity,and permutation invariance,and lacks topological information,which makes it difficult to extract features of point cloud.Therefore,a Siamese Adaptive Graph Convolution Algorithm(SAGCA)was proposed for point cloud classification and segmentation.Firstly,the topological relationships between irregular and sparse point cloud features were mined by constructing feature relationship graph.Then,the Siamese composition idea of sharing convolution learning weights was introduced to ensure the permutation invariance of point cloud data and make the topological relationship expression more accurate.Finally,SAGCA was combined with various deep learning networks for processing point cloud data by both global and local combination methods,thereby enhancing the feature extraction ability of the network.Comparison results with PointNet++benchmark network of the classification,object part segmentation and scene semantic segmentation experiments on ScanObjectNN,ShapeNetPart and S3DIS datasets,respectively,show that,based on the same dataset and evaluation criteria,SAGCA has the class mean Accuracy(mAcc)of classification increased by 2.80 percentage points,the overall class average Intersection over Union(IoU)of part segmentation increased by 2.31 percentage points,and the class mean Intersection over Union(mIoU)of scene semantic segmentation increased by 2.40 percentage points,verifying that SAGCA can effectively enhance the feature extraction ability of the network and is suitable for multiple point cloud classification and segmentation tasks.
作者
李维刚
陈婷
田志强
LI Weigang;CHEN Ting;TIAN Zhiqiang(School of Information Science and Engineering,Wuhan University of Science and Technology,Wuhan Hubei 430081,China;Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education(Wuhan University of Science and Technology),Wuhan Hubei 430081,China)
出处
《计算机应用》
CSCD
北大核心
2023年第11期3396-3402,共7页
journal of Computer Applications
基金
湖北省重点研发计划项目(2020BAB098)。
关键词
点云数据
拓扑关系
孪生
自适应图卷积
分类
分割
point cloud data
topological relationship
Siamese
adaptive graph convolution
classification
segmentation
