Point Cloud Classification and Segmentation Based on Graph Walk and Graph Attention

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Point Cloud Classification and Segmentation Based on Graph Walk and Graph Attention

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[1]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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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 02 Page number: 33-41 Column: Public date: 2024-03-06

Title:: Point Cloud Classification and Segmentation Based on Graph Walk and Graph Attention

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

Keywords:: point cloud classification; point cloud segmentation; graph neural network; graph walk; graph attention mechanism

CLC:: TP391. 41；TP183

DOI:: 10.13705/j.issn.1671-6833.2024.02.006

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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Last Update: 2024-03-08