Confidence-Based Surface Prior for Energy-Minimization Stereo Matching

Abstract

This paper presents a novel confidence-based surface prior for energy minimization formulations of dense stereo matching. Given a dense disparity estimation we fit planes, in disparity space, to regions of the image. For each pixel, the probability of its depth lying on an object plane is modeled as a Gaussian distribution, whose variance is determined using the confidence from a previous matching. We then recalculate a new disparity estimation with the addition of our novel confidence-based surface prior. The process is then repeated. Unlike many region-based methods, our method defines an energy formulation over pixels, instead of regions in a segmentation; this results in a decreased sensitivity to the quality of the initial segmentation. Our confidence-based surface prior differs from existing surface constraints in that it varies the per-pixel strength of the constraint to be proportional to the confidence in our given disparity estimation. The addition of our surface prior has three main benefits: sharp object-boundary edges in areas of depth discontinuity; accurate disparity in surface regions; and low sensitivity to segmentation. We evaluate our method using Middlebury stereo sets and more challenging remote sensing data. Our experimental results demonstrate that our approach has superior performance on these data sets.