Segmentation is a low-level vision cue often deployed by stereo algorithms to assume that disparity within superpixels varies smoothly. In this paper, we show that constraining, on a superpixel basis, the cues provided by a recently proposed technique [1], which explicitly models local consistency among neighboring points, yields accurate and dense disparity fields. Our proposal, starting from the initial disparity hypotheses of a fast dense stereo algorithm based on scanline optimization, demonstrates its effectiveness by enabling us to obtain results comparable to top-ranked algorithms based on iterative disparity optimization methods.

S. Mattoccia, "Accurate dense stereo by constraining local consistency on superpixels", 20th International Conference on Pattern Recognition (ICPR2010), August 23-26, 2010, Istanbul, Turkey [Pdf] [Supplementay_material] [Bibtex] 


Under construction...




Experimental results according to the Middlebury automatic evaluation [2,3] deploying the initial disparity hypotheses provided by algorithm [4], available in [3] and  referred to as D_S. The output of the proposed algorithm is referred to as D⁽³⁾. Further details can be found in the paper and in the slides linked at the bottom of this page.








Complete experimental results on the Venus stereo pair [2,3] using the initial disparity hypothesses provided by [4] and available on the same website. For segmentation I deployed the Mean-Shift algorithm [5] (source code available in [6]).