Confidence-based iterative efficient large-scale stereo matching

In this study, we integrate confidence into efficient large-scale stereo (ELAS) matching to produce a more accurate approach to binocular stereo for high-resolution image matching. Elas ensures good performance in the presence of poorly textured and slanted surfaces, but one of its deficiencies is its unsatisfactory ability to capture disparity discontinuities. Our formulation explicitly models the effects of confidence as a likelihood term in a principled manner using the Bayes rule. Because it is an iterative method, we associate each point with a variable confidence value and update this value based on a given confidence updating rule. Meanwhile, complementary support points are selected from stable points whose confidence value exceeds a predefined threshold, which differs from ELAS, whose support points are matched in advance and kept unchanged in the subsequent process. Confidence also plays a vital role in avoiding expensive computation, and the adjustment of support points makes disparity estimation more flexible. Quantitative evaluation demonstrates the effectiveness and efficiency of the proposed formulation in improving the accuracy of disparity estimation