Accurate stereo matching based on weighted nonlocal aggregation for enhanced disparity refinement

Disparity refinement is an important step to enhance the accuracy of stereo matching. This paper extends the scheme of a recent successful approach, namely the nonlocal disparity refinement algorithm, to exploit the initial disparity map in the aggregation phase of disparity refinement, in addition to the information of spatial distance and intensity difference. In addition, we propose a constraint function applied to the matching cost that constrains the scope of dissimilarity measures to further improve the accuracy of disparity refinement. Extensive experimental comparisons with several state-of-the-art methods using the Middlebury Stereo Evaluation version 3 datasets show that the proposed scheme has a great advantage in disparity refinement.