VEHICLE COMPLETION IN TRAFFIC SCENE USING 3D LIDAR POINT CLOUD DATA

Modern autonomous vehicles perceive their surroundings with the help of 3D computer vision technologies (e.g., object detection, vehicle recognition). The 3D point cloud obtained by vehicle-mounted mobile LiDAR is the primary data for 3D visual tasks in the driving system. Due to partial observations, a complete point cloud of the surrounding vehicle cannot be obtained. In this paper, we proposed Point Voxel Completion Network (PVCNet), an end-to-end learning-based model for 3D vehicle completion. Unlike existing point cloud completion methods, PVCNet only predicts the missing parts of the input and preserves the original spatial structure of the point cloud. PVCNet consists of two branches, the voxel branch and the point cloud branch. The voxel branch extracts the local features of the input and predicts the position of the missing point cloud in voxel form. The point cloud branch extracts the global features of the input point cloud. Combined with features from two branches, PVCNet can generate the missing points. The proposed PVCNet is validated on our hybrid dataset and KITTI dataset and performed competitive results in the task of urban vehicle point cloud completion.