Stereo-LiDAR Depth Estimation with Deformable Propagation and Learned
  Disparity-Depth Conversion
Ang Li Anning Hu Wei Xi Wenxian Yu Danping Zou
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 and a
confidence map by propagating sparse hints using a learned deformable window.
These maps then guide cost aggregation in stereo matching. To reduce the
triangulation error in depth recovery from disparity, especially in distant
regions, we introduce a disparity-depth conversion module. Our method is both
accurate and efficient. The experimental results on benchmark tests show its
superior performance. Our code is available at
https://github.com/SJTU-ViSYS/SDG-Depth.