Evaluation of Stereo Confidence Indoors and Outdoors

Abstract

We present an extensive evaluation of 13 confidence metrics for stereo matching that compares the most widely used metrics as well as four novel techniques proposed here. We begin by categorizing the methods according to which aspects of stereo computation they take into account and, then, assess their strengths and weaknesses. The evaluation is conducted on indoor and outdoor datasets with ground truth and measures the capability of each confidence metric to rank depth estimates according to their likelihood for being correct, to detect occluded pixels and to generate low-error depth maps by selecting among multiple hypotheses for each pixel. We believe that such an evaluation is missing from the rapidly maturing stereo literature and that our findings will be helpful to researchers in binocular and multi-view stereo.