Confidence stereo matching using complementary tree structures and global depth-color fitting

In this paper, a new stereo matching algorithm is proposed that separately estimates high and low-confidence region disparities. First, a mutually complementary tree structure decides on the high-confidence region, estimating its disparity map using dynamic programming optimization. Later, a disparity fitting algorithm restores low-confidence region disparity using high-confidence disparity and the information from one view color, fulfilling global optimization. The confidence stereo matching algorithm enhances both the occlusion areas and difficult-to-estimate such as thin objects, resulting in a high-quality disparity map.