Stereo Similarity Metric Fusion Using Stereo Confidence

Stereo confidence measures are one of the most popular research topics in stereo vision. These measures give an indication about the certainty of the matching. The main aim of using confidence measures is to filter the erroneous disparity estimations at the end of the matching process. However, they can also be incorporated at the initial step of the matching process to obtain accurate estimations before the cost aggregation. In this paper, we propose to utilize stereo confidence measures for fusing different similarity measures in order to obtain robust estimations for aggregation. Since stereo similarity measures perform differently in varying conditions, the confidence-guided fusion of them makes stereo matching more robust against errors. We evaluate the performance of our algorithm in comparison to different similarity measures on the Middleburry benchmark stereo test set. The results show significant improvements on the accuracy of initial disparity estimations with our fusion strategy compared to different similarity measures.