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Geometric Context from a Single Image

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

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Abstract

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
@conference{Hoiem-2005-9324,
title = {Geometric Context from a Single Image},
author = {Derek Hoiem and Alexei A. Efros and Martial Hebert},
booktitle = {International Conference of Computer Vision (ICCV)},
keyword = {geometry, context, object detection},
publisher = {IEEE},
school = {Robotics Institute , Carnegie Mellon University},
month = {October},
year = {2005},
volume = {1},
pages = {654 - 661},
address = {Pittsburgh, PA},
}
2017-09-13T10:43:08+00:00