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Stereo 3D Reconstruction using Prior Knowledge of Indoor Scenes


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.

Fig1. Input images

Fig2. The result of reconstruction with the proposed method


  1. Kentaro Kofuji, Yoshihiro Watanabe, Takashi Komuro, and Masatoshi Ishikawa: Stereo 3D Reconstruction using Prior Knowledge of Indoor Scenes, 2011 IEEE International Conference on Robotics and Automation (ICRA 2011) (Shanghai, 2011.5.10)/Proceedings, pp.5198-5203.
Ishikawa Senoo Laboratory, Department of Information Physics and Computing, Department of Creative Informatics,
Graduate School of Information Science and Technology, University of Tokyo
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