DDGCN: graph convolution network based on direction and distance for point cloud learning

Point cloud is usually used to construct the surface shape of three-dimensional geometric objects. Due to the disorder and irregularity of the point cloud, it is still a challenge to fully acquire the semantic features of the point cloud. With the development of graph neural network and graph convolution neural network, researchers are integrating point cloud and graph structure to better represent the semantic features of the point cloud. In this paper, we propose a novel graph convolutional neural network that integrates distance and direction (DDGCN), which constructs a dynamic neighborhood graph by obtaining the similarity matrix of the point cloud, and then uses several multi-layer perceptrons to obtain the local features of the point cloud. For the sake of making the intra-classes in the point cloud data more compact and the spacing between classes larger than the intra-class spacing, we propose a new loss function combined with center loss. The proposed DDGCN has been tested on ModelNet40 dataset, ShapeNet Part dataset and S3DIS dataset, and has achieved state-of-the-art performance in both classification and segmentation tasks.