A novel GCN-based point cloud classification model robust to pose variances

Point cloud data can be produced by many depth sensors, such as Light Detection and Ranging (LIDAR) and RGB-D cameras, and they are widely used in broad applications of robotic navigation and remote -sensing for the understanding of environment. Hence, new techniques for object representation and clas-sification based on 3D point cloud are becoming increasingly in high demand. Due to the irregularity of the object shape, the point cloud-based object recognition is a very challenging task, especially the pose variances of a point cloud will impose many difficulties. In this paper, we tackle the challenge of pose variances in object classification based on point cloud by developing a novel end-to-end pose robust graph convolutional network. Technically, we first represent the point cloud using the spherical system instead of the traditional Cartesian system for simplicity of computation and representation. Then a pose auxiliary network is constructed with an aim to estimate the pose changes in terms of rotation angles. Finally, a graph convolutional network is constructed for object classification against the pose variations of point cloud. The experimental results show the new model outperforms the existing approaches (such as PointNet and PointNet++) on the classification task when conducting experiments on both the Model-Net40 and the ShapeNetCore dataset with a series of random rotations of a 3D point cloud. Specifically, we obtain 73.02% accuracy for classification task on the ModelNet40 with delaunay triangulation algo-rithm, which is much better than the state of the art algorithms, such as PointNet and PointCNN. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This study addresses the challenge of pose variations in object classification based on point cloud by developing a novel end-to-end pose robust graph convolutional network. Experimental results show that the new model outperforms existing approaches when conducting experiments on random rotations of 3D point clouds in ModelNet40 and ShapeNetCore datasets.