Distinctive Similarity Measure for Stereo Matching Under Point Ambiguity

International audienceThe point ambiguity owing to the ambiguous local appearances of image points is one of the main causes making the stereo problem difficult. Under the point ambiguity, local similarity measures are easy to be ambiguous and this results in false matches in ambiguous areas. In this paper, we present a new similarity measure to resolve the point ambiguity problem based on the idea that the distinctiveness, not the interest, is an appropriate criterion for feature selection under the point ambiguity. Here, the interest of a point represents how much information a point has for facilitating matching, while the distinctiveness of a point represents how much a point is distinguishable from other points. The proposed similarity measure named the Distinctive Similarity Measure (DSM) is essentially based on the distinctiveness of image points and the dissimilarity between them, which are both closely related to the local appearances of image points; the distinctiveness of an image point is related to the probability of a mismatch while the dissimilarity is related to the probability of a good match. We verify the efficiency of the proposed DSM by using testbed image sets. Experimental results prove that the proposed DSM is very effective for both semi-dense and dense stereo matching and considering the point distinctiveness in both images can improve the performance of stereo methods under the point ambiguity

On the confidence of stereo matching in a deep-learning era: a quantitative evaluation

Stereo matching is one of the most popular techniques to estimate dense depth maps by finding the disparity between matching pixels on two, synchronized and rectified images. Alongside with the development of more accurate algorithms, the research community focused on finding good strategies to estimate the reliability, i.e. the confidence, of estimated disparity maps. This information proves to be a powerful cue to naively find wrong matches as well as to improve the overall effectiveness of a variety of stereo algorithms according to different strategies. In this paper, we review more than ten years of developments in the field of confidence estimation for stereo matching. We extensively discuss and evaluate existing confidence measures and their variants, from hand-crafted ones to the most recent, state-of-the-art learning based methods. We study the different behaviors of each measure when applied to a pool of different stereo algorithms and, for the first time in literature, when paired with a state-of-the-art deep stereo network. Our experiments, carried out on five different standard datasets, provide a comprehensive overview of the field, highlighting in particular both strengths and limitations of learning-based strategies.Comment: TPAMI final versio