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We propose a new method of indoor-scene stereo vision that uses probabilistic prior knowledge of indoor scenes in order to exploit the global structure of artificial objects. In our method, we assume three properties of the global structure - planarity, connectivity, and parallelism/orthogonality - and we formulate them in the framework of maximum a posteriori (MAP) estimation. To enable robust estimation, we employ a probability distribution that has both high peaks and wide flat tails. In experiments, we demonstrated that our approach can estimate shapes whose surfaces are not constrained by three orthogonal planes. Furthermore, comparing our results with those of a conventional method that assumes a locally smooth disparity map suggested that the proposed method can estimate more globally consistent shapes.