Abstract
Most published techniques for reconstructing scenes from stereo pairs follow a conventional strategy of searching for a single surface yielding the best correspondence between the images. The search involves specific constraints on surface continuity, smoothness, and visibility (occlusions) embedded in a matching score – typically an ad hoc linear combination of distinctly different criteria of signal similarity. The coefficients or weighing factors are selected empirically because they dramatically effect accuracy of stereo matching. The single surface assumption is also too restrictive – few real scenes have only one surface.
We introduce a paradigm of concurrent stereo that circumvents in part these problems by separating image matching from a choice of the 3D surfaces. Concurrent stereo matching first detects all likely matching 3D volumes instead of single best matches. Then, starting in the foreground, the volumes are explored, selecting mutually consistent optical surfaces that exhibit high point-wise signal similarity. Local, rather than global, surface continuity and visibility constraints are applied.
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Morris, J., Gimel’farb, G., Liu, J., Delmas, P. (2005). Concurrent Stereo Matching: An Image Noise-Driven Model. In: Rangarajan, A., Vemuri, B., Yuille, A.L. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2005. Lecture Notes in Computer Science, vol 3757. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11585978_4
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DOI: https://doi.org/10.1007/11585978_4
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