Stereo-LiDAR Depth Estimation with Deformable Propagation and Learned Disparity-Depth Conversion

arXiv:2404.07545v1 Announce Type: new
Abstract: Accurate and dense depth estimation with stereo cameras and LiDAR is an important task for automatic driving and robotic perception. While sparse hints from LiDAR points have improved cost aggregation in stereo matching, their effectiveness is limited by the low density and non-uniform distribution. To address this issue, we propose a novel stereo-LiDAR depth estimation network with Semi-Dense hint Guidance, named SDG-Depth. Our network includes a deformable propagation module for generating a semi-dense hint map …

arxiv conversion cs.cv lidar propagation type