LAUSR

Abstract 3D vehicle detection is a significant step for traffic scene understanding. Several recent works have achieved state-of-the-art performance with only LiDAR point clouds. However, there are still existing challenges due to the intrinsic limitations of lidar data. In this paper, we propose a novel 3D vehicle detection method, in which visual information is introduced to remedy the deficiency of sparse point clouds. Our method is composed of two stages. In first stage, a novel proposal generator with the guidance of visual information is proposed. In the proposal generator, 2D detected bounding boxes are registered with 3D candidates from LiDAR by calibrating and then weighted non-maximum suppression (WNMS) is applied to increase the confidence of proposals to remove redundant proposals. In second stage, to construct a box predictor, instead of sampling in a sphere space, a novel sampling with cylinder space for pointnet++ is leveraged to learn local features of the point clouds. Furthermore, to achieve a better balance between confidence and localization accuracy of boxes, an Intersection-over-Union (IoU) prediction branch is modified and attached to the network. We conduct multiple experiments on the KITTI 3D object detection dataset and compare our method with state-of-the-art methods. The comparison results show that our algorithm is effective and can improve the performance of 3D vehicle detection.