Stereo 3D Reconstruction using Prior Knowledge of Indoor Scenes

摘要：

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.

作者: Kentaro Kofuji Yoshihiro Watanabe Takashi Komuro Masatoshi Ishikawa

作者单位: Graduate School of Information Science and Technology,The University of Tokyo,7-3-1 Hongo,Bunkyo-ku,Tokyo 113-8656,Japan

会议类型: 国际会议

会议名称: 2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)

会议地点: 上海

会议语种:英文

页码: 5198-5203

在线出版日期: 2011-05-09（万方平台首次上网日期，不代表论文的发表时间）