Integration of Coordinate and Geometric Surface Normal for 3D Point Cloud Object Detection

LiDAR-based 3D object detection is important for numerous applications, such as autonomous driving and robot navigation. How to use the specialized geometric representation of point clouds, such as surface normal, to promote the performance of 3D detection is an essential but seldom explored issue. In this paper, we introduce a normal module using geometric surface normal representation to supplement existing methods using coordinate representation. We also propose an attention module for further integration. It integrates features learning from the two representations – coordinate and surface normal, so that they can make up for each other's feature space. Our framework can improve the object detection accuracy, especially for targets with complementary coordinate and surface normal features. As far as we know, we are the first work applying surface normal of LiDAR point cloud to 3D object detection, which can be easily extended to other LiDAR-based methods. Extensive experiments on the KITTI dataset demonstrate that our method boosts the performance of 3D object detection for the voxel-based methods and achieves state-of-the-art results.