Deep Learning Model for Point Cloud Classification Based on Graph Convolutional Network

Xujiao, 王旭娇 Wang; Jie, 马杰 Ma; Nannan, 王楠楠 Wang; Pengfei, 马鹏飞 Ma; Lichaung, 杨立闯 Yang

doi:10.3788/lop56.211004

Cited by 3 publications

(1 citation statement)

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“…pointNet++ [23] improved the feature extraction method to address this problem, but suffered from the problems of sensitivity to input noise and high time complexity. Methods [24][25] went one step further by constructing k-nearest neighbor graphs in the point cloud space to effectively obtain point cloud local information and reduced the time cost. On the other hand, pointRCNN [5] proposed a 3D target detection framework based on the original point cloud using PointNet++ as the backbone network, which was experimentally proven to have high robustness and accurate 3D detection performance.…”

Section: Related Workmentioning

confidence: 99%

3D Point Cloud Target Detection Method Based on R-PointGNN

Wang

et al. 2022

Preprint

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Point cloud target detection completes the interaction of 3D features such as vector position and reflection intensity in the coordinate system and 3D visualization with visual enhancement effects, which are widely used in virtual reality, augmented reality and autonomous driving. However, the disorder, sparsity and overlap of LiDAR point clouds increase the difficulty of point cloud recognition. To address this problem, a 3D point cloud target detection model named R-PointGNN (Residual-Graph Neural Network) based on graph neural network is proposed. Deep residual connections are constructed on the graph neural network structure. Firstly, the semantic graphs of the point clouds are constructed by the nearest neighbor algorithm; then, the feature transfer and state update of the graphs are completed by the improved edge convolution; finally, the residual connections are introduced to connect multiple layers of states and extract deep features. Experiments on the KITTI dataset show that the method performs well in the point cloud target detection task with a high degree of discrimination.

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Section: Related Workmentioning

confidence: 99%