An Effective Point Cloud Classification Method Based on Improved Non-local Neural Networks
- Resource Type
- Conference
- Authors
- Song, Yanan; Liu, Xianfei; Shen, Weiming; Gao, Yiping; Zhou, Xianke; Lu, Peng
- Source
- 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD) Computer Supported Cooperative Work in Design (CSCWD), 2022 IEEE 25th International Conference on. :665-670 May, 2022
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Transportation
Point cloud compression
Deep learning
Shape
Conferences
Computational modeling
Neural networks
Benchmark testing
Point cloud classification
Local information
non-local neural network
- Language
Deep learning is an important method to deal with point cloud, but its ability is limited to extract local features of point cloud. Many deep learning networks are designed to capture the local information, but they ignore the importance of non-local features to the point cloud. This paper proposes an improved non-local neural networks for point cloud classification. The non-local module can extract local and non-local features of the point cloud simultaneously. The local information is obtained based on the feature distance between neighborhood points searched by k-nearest neighbor method. The extracted local features are integrated into the non-local network, which can capture non-local features from the entire point cloud. The designed non-local module can be easily inserted into the existing point cloud processing network. The proposed method is evaluated on well-known ModelNet40 shape classification benchmark. Experimental results show that the proposed method achieves a significant improvement in classification accuracy.