Edge Convolution with Multi-Head Point Attention for Point Cloud Classification and Segmentation

A point cloud classification and segmentation model MHPAEC, which combines multi-head point attention and edge convolution, is proposed to address the problem of DGCNN only extracting point features independently at local scales without correlating local points. Firstly, design a single-head point attention module to calculate the attention features of the point cloud and the neighborhood attention features, learn the rotation invariance of the point cloud, use a multi-head mechanism to aggregate the single-head point attention modules, construct a multi-head point attention module, and assign corresponding attention coefficients to different points in the neighborhood. Secondly, design a weighted pyramid pooling module for feature fusion to obtain richer feature information. Finally, aiming at the problem of difficult to classify samples and category imbalance in the task of point cloud classification and segmentation, a joint loss function combining cross entropy loss and focal loss is proposed to make samples fully trained point cloud classification and segmentation experiments were conducted on the ModelNet40 dataset and ShapeNet dataset, respectively. On the ModelNet40 dataset, the overall accuracy of the MHPAEC network reached 94.1%; on the ShapeNet dataset, the mean intersection over union of the MHPAEC network reached 86.3%, effectively improving the classification and segmentation performance of the network model.