Point cloud semantic scene segmentation based on coordinate convolution

Point cloud semantic segmentation, a crucial research area in the 3D computer vision, lies at the core of many vision and robotics applications. Due to the irregular and disordered of the point cloud, however, the application of convolution on point clouds is challenging. In this article, we propose the coordinate convolution, which can effectively extract local structural information of the point cloud, to solve the inapplicability of conventional convolution neural network (CNN) structures on the 3D point cloud. The coordinate convolution is a projection operation of three planes based on the local coordinate system of each point. Specifically, we project the point cloud on three planes in the local coordinate system with a joint 2D convolution operation to extract its features. Additionally, we leverage a self-encoding network based on image semantic segmentation U-Net structure as the overall architecture of the point cloud semantic segmentation algorithm. The results demonstrate that the proposed method exhibited excellent performances for point cloud data sets corresponding to various scenes.