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. © 2005 IEEE.
CITATION STYLE
Hoiem, D., Efros, A. A., & Hebert, M. (2005). Geometric context from a single image. In Proceedings of the IEEE International Conference on Computer Vision (Vol. I, pp. 654–661). https://doi.org/10.1109/ICCV.2005.107
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