/Geometric Context from a Single Image

Geometric Context from a Single Image

Derek Hoiem, Alexei A. Efros and Martial Hebert
Conference Paper, International Conference of Computer Vision (ICCV), Vol. 1, pp. 654 - 661, October, 2005

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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.

BibTeX Reference
author = {Derek Hoiem and Alexei A. Efros and Martial Hebert},
title = {Geometric Context from a Single Image},
booktitle = {International Conference of Computer Vision (ICCV)},
year = {2005},
month = {October},
volume = {1},
pages = {654 - 661},
publisher = {IEEE},
keywords = {geometry, context, object detection},