  The performance of image based stereo estimation suffers from lighting
variations, repetitive patterns and homogeneous appearance. Moreover, to
achieve good performance, stereo supervision requires sufficient
densely-labeled data, which are hard to obtain. In this work, we leverage small
amount of data with very sparse but accurate disparity cues from LiDAR to
bridge the gap. We propose a novel sparsity expansion technique to expand the
sparse cues concerning RGB images for local feature enhancement. The feature
enhancement method can be easily applied to any stereo estimation algorithms
with cost volume at the test stage. Extensive experiments on stereo datasets
demonstrate the effectiveness and robustness across different backbones on
domain adaption and self-supervision scenario. Our sparsity expansion method
outperforms previous methods in terms of disparity by more than 2 pixel error
on KITTI Stereo 2012 and 3 pixel error on KITTI Stereo 2015. Our approach
significantly boosts the existing state-of-the-art stereo algorithms with
extremely sparse cues.
