Binocular stereovision estimates the three-dimensional shape of a scene from two photographs taken from different points of view. In rectified epipolar geometry, this is equivalent to a matching problem. This article describes a method proposed by Kolmogorov and Zabih in 2001, which puts forward an energy-based formulation. The aim is to minimize a four-term-energy. This energy is not convex and cannot be minimized except among a class of perturbations called expansion moves, in which case an exact minimization can be done with graph cuts techniques. One noteworthy feature of this method is that it handles occlusion: The algorithm detects points that cannot be matched with any point in the other image. In this method displacements are pixel accurate (no subpixel refinement). Source Code The software rewritten from Kolmogorov's code is available at the IPOL web page of this article 1. A set of stereo pairs is available and Kolmogorov and Zabih's algorithm can be tried on line. In the demo, the algorithm is run on six overlapping slices of the images, for efficiency purpose. Essentially, two parameters are needed: K associated to occlusion cost and λ to data fidelity. By default they are tuned automatically but they can be adapted to get better results. Supplementary Material In the demo, an optional rectification step can be launched before running the algorithm. The source code for this preprocessing step (not reviewed) can be found at the IPOL web page of this article 2 .

This paper extends recent results by the first author and T. Pock (ICG, TU Graz, Austria) on the acceleration of alternating minimization techniques for quadratic plus nonsmooth objectives depending on two variables. We discuss here the strongly convex situation, and how "fast" methods can be derived by adapting the overrelaxation strategy of Nesterov for projected gradient descent. We also investigate slightly more general alternating descent methods, where several descent steps in each variable are alternatively performed.

No abstract

International audienceEstimating 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. Source Code A software written in C++ is available on the IPOL web page of this article 1 , which is the code used in the online demo. This gives similar results to the original authors' Matlab implemen-tation 2 . The program needs several parameters (see Section 4 for more detailed explanations). By default they are tuned as suggested in the original article, but one can adapt them to get better results. Supplementary Material In the demo, an optional rectification step can be launched before running the algorithm. The source code for this preprocessing step (not reviewed) can be found at the IPOL web page of this article 3