Differential feature guidance and compressed cost volume for large disparity stereo network

Stereo matching is of vital importance for the perception system of autonomous driving. Although significant achievements have been made so far, matching blurring still easily occurs in large disparity and ill-posed regions. This paper introduces a novel stereo matching algorithm, DV-Stereo, which constructs differential features using the disparity map through a difference optimization module and Difference Guided Attention to optimizes the blurry regions in the correlation disparity features. Additionally, to make the model applicable to large disparity scenarios, a compressed cost volume is proposed in this paper. It can improve the model's performance in large disparity regions with a small increase in computational cost. Furthermore, to effectively fuse the correlation features in the compressed cost volume, an adaptive correlation feature fusion module is proposed. This module can adaptively fuse the geometric feature information under different disparity ranges and input it into the ConvGRU iterative optimization module. DV-Stereo performs well in benchmark tests on the Scene Flow, KITTI 2012 & 2015, and ETH3D datasets. Particularly on the ETH3D dataset, the 1-pixel error is only 0.90, which is approximately 43% lower than the state-of-the-art methods. Meanwhile, DV-Stereo demonstrates extremely strong performance in the generalization test on the Middlebury 2014 dataset.