The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Geometric context from a single image
Open accessAbstract
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.
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Cites
Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
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Hoiem, D., Efros, A. A., & Hebert, M. (2005) · ACM Transactions on Graphics · doi:10.1145/1073204.1073232 · Cited by 616
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Metadata
- Authors
- Hoiem, D., Efros, A. A., & Hebert, M.
- Venue
- Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1
- Publisher
- IEEE
- Published
- 2005
- Pages
- 654-661 Vol. 1
- OpenAlex
- W2155871590
- Cited by
- 662
- References
- 3
- FWCI
- 18.24
- Type
- conference paper
- Language
- en
- Open access
- green
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