SymStereo: Stereo Matching using Induced Symmetry

Stereo methods always require a matching function for assessing the likelihood of two pixels being in correspondence. Such functions, commonly referred as matching costs, measure the photo-similarity (or dissimilarity) between image regions centered in putative matches. This article proposes a new family of stereo cost functions that measure symmetry instead of photo-similarity for associating pixels across views. We start by observing that, given two stereo views and an arbitrary virtual plane passing in-between the cameras, it is possible to render image signals that are either symmetric or anti-symmetric with respect to the contour where the virtual plane meets the scene. The fact is investigated in detail and used as cornerstone to develop a new stereo framework that relies in symmetry cues for solving the data association problem. Extensive experiments in dense stereo show that our symmetry-based cost functions compare favorably against the best performing photo-similarity matching costs. In addition, we investigate the possibility of accomplishing Stereo Rangefinding that consists in using passive stereo to exclusively recover depth along a pre-defined scan plane. Thorough experiments provide evidence that stereo from induced symmetry is specially well suited for this purpose.