This paper describes a new algorithm for solving the N-camera stereo correspondence problem by transforming it into a maximum-flow problem. Once solved, the minimum-cut associated to the maximumflow yields a disparity surface for the whole image at once. This global approach to stereo analysis provides a more accurate and coherent depth map than the traditional line-by-line stereo. Moreover, the optimality of the depth surface is guaranteed and can be shown to be a generalization of the dynamic programming approach that is widely used in standard stereo. Results show improved depth estimation as well as better handling of depth discontinuities. While the worst case running time is O(n 2 d 2 log(nd)), the observed average running time is O(n 1:2 d 1:3 ) for an image size of n pixels and depth resolution d. 1 Introduction It is well known that depth related displacements in stereo pairs always occur along lines associated to the camera motion, the epipolar lines. These lines r...

This paper presents a new model to overcome the occlusion problems coming from wide baseline multiple camera stereo. Rather than explicitly modeling occlusions in the matching cost function, it detects occlusions in the depth map obtained from regular efficient stereo matching algorithms. Occlusions are detected as inconsistencies of the depth map by computing the visibility of the map as it is reprojected into each camera. Our approach has the particularity of not discriminating between occluders and occludees. The matching cost function is modified according to the detected occlusions by removing the offending cameras from the computation of the matching cost. The algorithm gradually modifies the matching cost function according to the history of inconsistencies in the depth map, until convergence. While two graph-theoretic stereo algorithms are used in our experiments, our framework is general enough to be applied to many others. The validity of our framework is demonstrated using real imagery with different baselines.

This paper presents a new and fast algorithm for multibaseline stereo designed to handle the occlusion problem. The algorithm is a hybrid between fast heuristic occlusion overcoming algorithms that precompute an approximate visibility and slower methods that use correct visibility handling. Our approach is based on iterative dynamic programming and computes simultaneously disparity and camera visibility. Interestingly, dynamic programming makes it possible to compute exactly part of the visibility information. The remainder is obtained through heuristics. The validity of our scheme is established using real imagery with ground truth and compares favorably with other state-of-the-art multi-baseline stereo algorithms.