Feature Relation based Graph Convolution for 3D Point Cloud Analysis

3D point cloud recognition is still a challenge task since the shape implied in irregular points is difficult to capture. Standard convolution is inherently limited for these tasks due to its isotropy about features. In this paper, a novel graph convolutional network is introduced for point cloud classification and segmentation task. The proposed convolution adds an additional layer on the basis of relation-shape convolutional neural network, which can obtain more information and make the representation of point cloud more robust. At the same time, a feature relation method is proposed instead of the coordinate relation used in relation-shape convolutional neural network. The experimental results on challenging classification and segmentation datasets shows that the proposed method can learn discriminating features for recognition and semantic segmentation.