Multi-channel depth segmentation network based on 3D graph convolution algorithm and its application in point cloud segmentation

At the current stage, 3D graph convolutional algorithms face the following issues: (1) The problem of determining the neighborhood in graph convolution. (2) The issue of fusing features at different depths in deep graph convolutional algorithms. (3) The challenge of feature fusion and recognition in multi-path deep graph convolutional algorithms. Based on these, the paper "Multi-Path Deep Segmentation Network Based on 3D Graph Convolutional Algorithm and Its Application in Point Cloud Segmentation" is proposed. Inspired by visual computing theory, the neighborhood for 3D graph convolution is determined based on the receptive field theory, and a 3D graph convolutional algorithm based on visual selectivity is proposed; A single-link deep feature extraction algorithm for 3D graph convolution based on visual selectivity is proposed, it achieve the fusion of features at different depths learned by the graph convolutional algorithm by a pyramid learning method; A multilink feature extraction algorithm for 3D graph convolution based on visual selectivity is proposed, and achieve multi-link feature fusion and segmentation to utilize the RPN calculation method. compared with algorithms such as PointNet, PointNet++, KPConv deform, 3D GCN, and My Algorithm, The segmentation performance and geometric invariance of the proposed algorithm are validated on the ShapeNetPart dataset and the custom Mortise_and_Tenon_DB dataset, and Experiments demonstrate that the proposed algorithm is correct and feasible, offers superior 3D point cloud segmentation performance, and exhibits strong geometric invariance.