Multi-branch global graph convolution network for point clouds

Abstract

For finding the defect inside of rails, we usually use the ultrasound to inspect the rails and obtain point clouds data of the returned ultrasonic signals after preprocessing. The quantity of points in each point cloud is not fixed, and the point clouds are disordered and unstructured. The points have heterogeneous attributes which include not only continuous coordinates but discrete frequencies of the ultrasound. For classifying the special point clouds, we propose a network architecture, named Multi-Branch Global Graph Convolution Network. For better utilizing the features of heterogeneous attributes of each point, we introduce the point channel-wise attention mechanism to weight the attributes. The backbone of the network is a multi-branch learn network based on the global graph convolution. By constructing the global graph and applying the graph convolution, the network possesses the ability to learn the local relative position information and global semantic information. Experimental results show the effectiveness of each component of the network and the state-of-the-art performance of the proposed network achieves on the classification task of defect recognition.