Rotation-aware 3D vehicle detection from point cloud

Abstract

Three-dimensional vehicle detection using LiDAR point clouds is important for the stability of autonomous driving. It can provide high-quality three-dimensional information for most obstacles. Although efficient algorithms based on a Bird's Eye View (BEV) feature map have been developed, many research issues still remain; in particular, the existing methods show a limited accuracy in estimating the rotation angle of a 3D object. In this paper, to improve the accuracy of rotation angle estimation, we propose a rotation-aware 3D vehicle detector that extracts distinguishable features from the proposals with various angles of rotations. Experiments are conducted on KITTI dataset and Waymo Open dataset. Our approach improves detection accuracy as well as rotation angle estimation accuracy against the existing algorithms without much loss of computational efficiency.