Monocular Pseudo-LiDAR Point Cloud Extrapolation Based on Iterative Hybrid Rendering

Recently, a pseudo-LiDAR point cloud extrapolation algorithm equipped with stereo cameras has been introduced, bridging the gap between the expensive 3D sensor LiDAR and relatively cheap 2D sensor camera in autonomous driving. In this paper, we explore an approach to further bridge this gap using only a monocular camera and extrapolate a wide field of view 3D point cloud from a limited 2D view. However, this task is extremely challenging as it requires inferring the occluded contents in the scene. To this end, we propose a 'render-refine-iterate-fuse' framework that takes advantage of both image view synthesis and image inpainting techniques, guiding the neural network to learn the potential spatial distribution. In addition, we design a hybrid rendering scheme to ensure that the visible content moves in a geometrically correct manner and fills the pixels caused by occlusion. Benefitting from the proposed framework, our approach achieves significant improvements on the pseudo-LiDAR point cloud extrapolation task. The gap between LiDAR and cameras is further bridged, showing an economical and practical application in the environment perception module of autonomous driving. The experimental results evaluated on the KITTI dataset demonstrate that our approach achieves superior quantitative and qualitative performance.