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A Higher Order MRF-Model for Stereo-Reconstruction

机译:立体重构的高阶MRF模型

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We consider the task of stereo-reconstruction under the following fairly broad assumptions. A single and continuously shaped object is captured by two uncalibrated cameras. It is assumed, that almost all surface points are binocular visible. We propose a statistical model which represents the surface as a triangular (hexagonal) mesh of pairs of corresponding points. We introduce an iterative scheme, which simultaneously finds an optimal mesh (with respect to a certain Bayes task) and a corresponding optimal fundamental matrix (in a maximum likelihood sense). Thus the surface is reconstructed up to a projective transform.
机译:我们在以下相当广泛的假设下考虑立体声重建的任务。由两个未校准的相机捕获单个且连续成形的物体。假定几乎所有表面点都是双目可见的。我们提出了一个统计模型,该模型将表面表示为成对的相应点的三角形(六边形)网格。我们引入一个迭代方案,该方案同时找到一个最佳网格(相对于某个贝叶斯任务)和一个相应的最佳基本矩阵(在最大似然意义上)。因此,将曲面重建到投影变换。

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