[1]李文举,姬倩倩,沙利业,等.基于图游走和图注意力的点云分类与分割[J].郑州大学学报(工学版),2024,45(02):33-41.[doi:10.13705/j.issn.1671-6833.2024.02.006]
　LI Wenju,JI Qianqian,SHA Liye,et al.Point Cloud Classification and Segmentation Based on Graph Walk and Graph Attention[J].Journal of Zhengzhou University (Engineering Science),2024,45(02):33-41.[doi:10.13705/j.issn.1671-6833.2024.02.006]
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基于图游走和图注意力的点云分类与分割()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
45
期数:
2024年02期
页码:
33-41
栏目:
出版日期:
2024-03-06

文章信息/Info

Title:
Point Cloud Classification and Segmentation Based on Graph Walk and Graph Attention
作者:
李文举1;  姬倩倩1;  沙利业2;  储王慧1;  崔 柳1
1. 上海应用技术大学 计算机科学与信息工程学院,上海 201418;2. 上海普利森配料系统有限公司,上海 201108
Author(s):
LI Wenju1;  JI Qianqian1;  SHA Liye2;  CHU Wanghui1;  CUI Liu1
1. School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai 201418, China; 2. Shanghai Precision Dosing & Weighing System Co. , Ltd. , Shanghai 201108, China
关键词:
点云分类;  点云分割;  图神经网络;  图游走;  图注意力机制
Keywords:
point cloud classification;  point cloud segmentation;  graph neural network;  graph walk;  graph attention mechanism
分类号:
TP391. 41；TP183
DOI:
10.13705/j.issn.1671-6833.2024.02.006
文献标志码:
A
摘要:
针对点云特征提取中远距离特征和局部几何结构信息欠缺的问题,提出了一种基于图游走和图注意力的 点云分类与分割网络。 首先,利用带有导向性的图游走算法,对点云全局特征补充额外的几何信息和远距离特征 信息;其次,嵌入图注意力机制,使模型聚焦于点云的关键区域,提升网络的特征提取能力;最后,在初始点云中提 取距离特征作为初始残差嵌入到网络中,避免网络过平滑。 在 ModelNet40 数据集、ScanObjectNN 数据集进行了点 云分类实验,在 ShapeNetPart 数据集与 Toronto-3D 数据集上分别进行了点云部件分割与点云语义分割实验,实验结 果表明:相较于基准网络 DGCNN,分类精度分别提升了 1. 3 百分点、5. 6 百分点;分割精度分别提升了 1. 2 百分点、 33. 1 百分点。 通过在 ModelNet40-C 数据集上进行稳健性分析,验证了所提网络具有较强的鲁棒性。
Abstract:
Aiming at the shortage of distance feature and local geometric structure information in feature extraction, a point cloud classification and segmentation network based on graph walk and graph attention was proposed. Firstly, a guided graph walk algorithm was used to supplement additional geometric information and remote feature information to the whole feature of point cloud. Secondly, the graph attention mechanism was embedded to make the model on the key areas of the point cloud and improve the feature extraction ability of the network. Finally, distance features were extracted from the initial point cloud and embedded into the network as initial residuals to avoid oversmoothing. Point cloud classification experiments were carried out on ModelNet40 dataset and ScanObjectNN dataset, and point cloud component segmentation and point cloud semantic segmentation experiments were carried out on ShapeNetPart dataset and Toronto-3D dataset, respectively. The experiment results showed that, compared with the benchmark network DGCNN, classification accuracy increased by 1. 3 percentages and 5. 6 percentages, respectively; The segmentation accuracy was improved by 1. 2 percentages and 33. 1 percentages respectively. Through the robust analysis on ModelNet40-C dataset, it was proved that the proposed network had strong robustness.

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更新日期/Last Update: 2024-03-08