Point Cloud Segmentation Algorithm Based on Residual Multi-perceptron and Spatial Attention
Existing 3D point cloud segmentation methods generally use multi-layer perceptrons as point cloud feature extrac-tors to achieve point cloud segmentation.However,the feature extractor fails to take into account the relationship between points in the point cloud,resulting in a weak ability to extract point cloud features.In order to fully learn the relationship between points and improve the accuracy of point cloud segmentation,this paper proposes a neural network that combines residual multi-perceptron and spatial attention to achieve the segmentation effect of 3D point cloud named ResPoint++.The ResPoint++network extracts the geometric and structural features of the local point cloud through multiple feature extraction modules containing residual multi-per-ceptron models,and on this basis,introduces a three-dimensional spatial attention mechanism to learn the relationship between lo-cal points and optimize network training.The final output is the category of each point in the dataset.The experimental results show that the point cloud segmentation network using ResPoint++has higher segmentation accuracy than PointNet and PointNet++,which verifies that the network has good point cloud segmentation performance.
point cloudspatial attentionsegmentationresidual MLPdeep learning
国家科技期刊平台
NSTL
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