Accurate and efficient stereo matching with robust piecewise voting
In this paper, we propose an efficient local stereo algorithm for accurate disparity estimation. First, we attain initial disparity estimates by iterating a cross-based cost aggregation process. Then, we propose a robust voting scheme to refine the initial estimates based on a piecewise smoothness prior, improving the quality in occluded regions and low-textured regions effectively. The refinement is guided by the segmentation result of input images. Unreliable initial estimates, which are detected using an efficient left-right consistency check, are rejected to increase the reliability of the voting results. Evaluated with the Middlebury stereo benchmark, our method is among the top performing local methods in accuracy. Compared to other local methods with similar accuracy, our method is faster by a factor of about two orders.