Region-Aware Driven Distribution Optimization for Stereo Matching

Accurate disparity estimation in diverse and complex scenes remains a significant challenge in stereo matching, requiring precise geometric perception and robust generalization. Traditional methods often struggle in capturing fine-grained details and maintaining structural consistency under varying conditions, leading to a great reduction of the disparity estimation performance. To address these limitations, we propose Reg-Stereo, a novel framework based on region-aware distribution optimization. It leverages region-aware awakening to extract structural cues and explicitly optimizes the spatial distribution of feature responses. By strengthening relative structural attributes within local regions and expanding them to the global context, our approach enables a more precise and context-aware representation of geometric structures, effectively capturing fine details while preserving global consistency. This innovative approach enables the framework to adapt effectively to diverse and challenging environments, improving both robustness and generalization. Extensive experiments on multiple datasets validate the effectiveness of Reg-Stereo, surpassing exisitng state-of-the-art methods in disparity estimation with enhanced adaptability across complex and heterogeneous scenarios.