Disparity Refinement Based on Cross-Modal Feature Fusion and Global Hourglass Aggregation for Robust Stereo Matching

Stereo matching is a critical research area in computer vision. The advancement of deep learning has led to the gradual replacement of cost-filtering methods by iterative optimization techniques, characterized by outstanding generalization performance. However, cost volumes constructed solely through recurrent all-pairs field transforms in iterative optimization methods lack adequate image information, making it challenging to resolve blurring issues in pathological regions such as illumination changes or similar textures. In this paper, we propose SCA-Stereo, a disparity refinement network aimed at further optimizing the initial disparity map generated by iteration. First, we introduce a high- and low-frequency feature extractor to delve deeper into the structural and fine feature information inherent in the image. Furthermore, we propose a cross-modal feature fusion module to facilitate the exchange and integration of diverse features, expanding the receptive field to enhance information flow. Finally, we design a global hourglass aggregation network to efficiently capture non-local interactions between fusion features. Extensive experiments conducted across Scene Flow, KITTI, Middlebury, and ETH3D demonstrate the effectiveness of SCA-Stereo in achieving state-of-the-art stereo matching performance.