Abstract

One of the major challenges in stereo matching is to handle partial occlusions. In this paper, we introduce the Outlier Confidence (OC) which dy- namically measures how likely one pixel is occluded. Then the occlusion infor- mation is softly incorporated into our model. A global optimization is applied to robustly estimating the disparities for both the occluded and non-occluded pix- els. Compared to color segmentation with plane fitting which globally partitions the image, our OC model locally infers the possible disparity values for the out- lier pixels using a reliable color sample refinement scheme. Experiments on the Middlebury dataset show that the proposed two-frame stereo matching method performs satisfactorily on the stereo images.