Abstract
Obtaining accurate disparity values in textureless and texture-free regions is a very challenging task. To solve this problem, we present a novel algorithm. First, we use the guided filter method to fuse the color cost volume and the gradient cost volume. Second, we use three types of image category information to merge the different scale disparity maps and obtain the primary disparity map. Third, during the disparity refinement procedure, we also utilize the three types of category information to define different support regions and assign different weights for pixels remaining to be refined. Extensive experiments show that the performance of our method is not inferior to many state-of-the-art methods on the Middlebury data set.
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