Stereo Disparity through Cost Aggregation with Guided Filter

Abstract

Estimating the depth, or equivalently the disparity, of a stereo scene is a challenging problem in computer vision. The method proposed by Rhemann et al. in 2011 is based on a filtering of the cost volume, which gives for each pixel and for each hypothesized disparity a cost derived from pixel-by-pixel comparison. The filtering is performed by the guided filter proposed by He et al. in 2010. It computes a weighted local average of the costs. The weights are such that similar pixels tend to have similar costs. Eventually, a winner-take-all strategy selects the disparity with the minimal cost for each pixel. Non-consistent labels according to left-right consistency are rejected; a densification step can then be launched to fill the disparity map. The method can be used to solve other labeling problems (optical flow, segmentation) but this article focuses on the stereo matching problem.