Disparity adapted weighted aggregation for local stereo

We present a novel aggregation method based on adaptive support weights for local stereo matching. In order to correctly match each block, the adaptive weight distribution should favor pixels sharing the same disparity. State of the art algorithms make this configuration depend only on spatial and color differences, identifying disparity discontinuities with color ones. Compared to these algorithms, we introduce a weight support depending on each tested disparity and not only on the image configuration around the reference pixel. For each tested disparity, we favor pixels in the block matching with smaller cost, which are supposed to be more likely to be correctly represented by this disparity. Besides, we use a multiscale strategy with invalidation criteria to reduce match ambiguity and computational time. Results on the Middlebury stereo benchmark show the performance improvement of the proposed approach in comparison with state of the art.