CAR-Stereo: Confidence-Aware Adaptive Disparity Refinement for Real-Time Stereo Matching

Abstract

In this paper, we propose a novel real-time disparity refinement method that enables precise structure perception. We construct a compact full-resolution cost volume from residuals around the initial disparity and adaptively eliminate redundant information on a per-pixel basis by leveraging the confidence. The core idea of our method comprises residual cost volume construction and an adaptive range masking strategy. The residual cost volume is constructed from refinement candidates around the initial disparity, based on the assumption that the ground-truth disparity is near the initial disparity. Compared to the conventional cost volume constructed over the entire set of disparity candidates, our approach achieves computational efficiency and maintains precise structural information by operating at full-resolution. Moreover, we propose an adaptive range masking strategy that filters refinement candidates for each pixel by leveraging confidence values. This approach effectively eliminates redundant information present in cost volumes that are composed of uniformly sampled refinement candidates. Experimental results on the Scene Flow and KITTI 2012 benchmarks demonstrate that our method achieves real-time performance and sets a new state-of-the-art among real-time stereo matching algorithms.