Adaptive Hint Propagation for Iterative Stereo Matching

Anning Hu, Ang Li, Danping Zou

Abstract: While sparse depth hints from LiDAR points have been utilized as guidance to enhance stereo matching, the improvement is hindered by the low density and uneven distribution of those points. To deal with these challenges, the sparse LiDAR hints are usually expanded for further processing. However, existing methods use only the local information of a fixed window surrounding the sparse hint, leading to inaccurate propagation that ultimately deteriorates stereo matching results. We introduce a new adaptive LiDAR propagation recurrent network by incorporating global context and local information, propagating the hints with an adaptive deformable window, and iteratively updating a disparity field through a recurrent unit. We have conducted comprehensive experiments on various public datasets. The results show that our method produces better matching quality than existing methods.