A Novel CNN Architecture for Real-Time Point Cloud Recognition in Road Environment

Abstract

To ameliorate the problems of disorder, sparseness, and floating occur for 3D LiDAR point cloud in the road environment, we propose a novel deep CNN architecture for real-time point cloud features extraction. Specifically, we first code the 3D position of point cloud by the index of vertical and horizontal directions. In this way, the 3D point cloud can be converted into a multi-channel point feature map. Then, through multi-level features extraction and fusion of the point feature map, the semantic segmentation of the point cloud scene is finally realized. Comprehensive experiments and ablation studies on public available point cloud datasets demonstrate the superiority of our approach. More importantly, our approach has been successfully applied to the perception of the real-world self-driving system. The source code has been made public available at: https://github.com/Lab1028-19/A-Novel-CNN.