Confidence Estimation for Superpixel-Based Stereo Matching

In this paper we propose an approach for estimating the confidence of stereo matches for super pixel-based disparity estimation. To our knowledge, this is the first such method reported in the literature. Starting from a simple super pixel stereo algorithm, we present a representative set of features that can be extracted from the disparity map and the super pixel fitting process. A random forest classifier is then trained on these features to predict whether the disparity assigned to each pixel of a test disparity map is correct or not. We perform experiments on the KITTI stereo benchmark and show that our confidence estimator is very accurate in predicting which disparities are correct and which are not. We also present a post-processing algorithm for improving the accuracy of the disparity maps that exploits the confidence estimates to reject wrong disparity values and achieves significant error reduction.