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Geometric Context from a Single Image
D. Hoiem, A.A. Efros, and M. Hebert
International Conference of Computer Vision (ICCV), IEEE, Vol. 1, October, 2005, pp. 654 - 661.

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Many computer vision algorithms limit their performance by ignoring the underlying 3D geometric structure in the image. We show that we can estimate the coarse geometric properties of a scene by learning appearance-based models of geometric classes, even in cluttered natural scenes. Geometric classes describe the 3D orientation of an image region with respect to the camera. We provide a multiple-hypothesis framework for robustly estimating scene structure from a single image and obtaining confidences for each geometric label. These confidences can then be used to improve the performance of many other applications. We provide a thorough quantitative evaluation of our algorithm on a set of outdoor images and demonstrate its usefulness in two applications: object detection and automatic single-view reconstruction.

Associated center: VASC
Associated lab/group: Face Group
Associated projects: Object Recognition Using Statistical Modeling and Geometrically Coherent Image Interpretation

Number of pages: 8

D. Hoiem, A.A. Efros, and M. Hebert, "Geometric Context from a Single Image," International Conference of Computer Vision (ICCV), IEEE, Vol. 1, October, 2005, pp. 654 - 661.

@inproceedings{Hoiem_2005_5164,
   author = "Derek Hoiem and Alexei A. Efros and Martial Hebert",
   title = "Geometric Context from a Single Image",
   booktitle = "International Conference of Computer Vision (ICCV)",
   month = "October",
   year = "2005",
   volume = "1",
   pages = "654 - 661",
   publisher = "IEEE"
}