WCG-Net: Warping Consistency Compensation Guided Multi-Feature Fusion For Stereo Matching

Despite the significant progress achieved by iterative optimization-based stereo matching methods, a critical challenge persists: these state-of-the-art models continue to face difficulties when handling ill-posed regions. This stems from lighting variations and viewpoint differences, which may cause the feature distributions extracted from the left and right images to differ, resulting in unreliable cost volume construction in ill-posed regions. To remedy this issue, we propose a novel network for stereo matching, named WCG-Net. In WCG-Net, we develop a warping consistency compensation module (WCCM) that employs consistency attention to identify feature differences and generate cross-view features, which are then used to construct the warping correlation volume. By introducing warping correlation volume into warping-guided fusion recurrent unit (WFRU), our method refines the disparity map iteratively, focusing on correcting errors in ill-posed regions. Extensive experimental evaluation demonstrates that WCG-Net achieves competitive results on the KITTI and ETH3D datasets, with particularly superior performance compared to other state-of-the-art algorithms on the KITTI dataset.