PIPC-3Ddet: Harnessing Perspective Information and Proposal Correlation for 3D Point Cloud Object Detection

Chuanbo Yu, Bo Peng, Qingming Huang, Jianjun Lei

Abstract: As a fundamental technology in autonomous driving and robotic sensing system, 3D point cloud object detection has received increasing attention. In this paper, a novel 3D detection method that harnesses perspective information and proposal correlation (PIPC-3Ddet) is proposed for detecting 3D objects from point clouds. Specifically, a perspective information embedding module is designed to enhance the voxel features by capturing and embedding the perspective information of range images, so as to effectively distinguish the objects and backgrounds. Besides, by revealing the correlation among 3D proposals, a proposal correlation reasoning module is presented to learn high-quality proposal features for better 3D proposal refinement. With the designed perspective information embedding and proposal correlation reasoning modules, the proposed PIPC-3Ddet is able to better perceive the objects in the 3D scene, thus boosting the 3D object detection performance. Extensive experiments on the KITTI and Waymo benchmarks have demonstrated the superiority of the proposed PIPC-3Ddet.