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Multiple View Geometry in Computer Vision
Abstract
A basic problem in computer vision is to understand the structure of a real world scene given several images of it. Techniques for solving this problem are taken from projective geometry and photogrammetry. Here, the authors cover the geometric principles and their algebraic representation in terms of camera projection matrices, the fundamental matrix and the trifocal tensor. The theory and methods of computation of these entities are discussed with real examples, as is their use in the reconstruction of scenes from multiple images. The new edition features an extended introduction covering the key ideas in the book (which itself has been updated with additional examples and appendices) and significant new results which have appeared since the first edition. Comprehensive background material is provided, so readers familiar with linear algebra and basic numerical methods can understand the projective geometry and estimation algorithms presented, and implement the algorithms directly from the book.
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Taylor, P., Ferria, C., Mastrogiacomob, L., Farawayc, J., Ferria, C., Mastrogiacomob, L., & Farawayc, J. (2003) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192031000075962 · Cited by 491
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Zhao, M., Tian, Y., Zhao, H., Alsheikh, M. A., Li, T., Hristov, R., Kabelac, Z., Katabi, D., & Torralba, A. B. (2018) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/3230543.3230579 · Cited by 378
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Metadata
- Authors
- Hartley, R., & Zisserman, A.
- Affiliations
- Australian National University (AU)
- University of Oxford (GB)
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2004-03-25
- OpenAlex
- W2033819227
- Cited by
- 21,070
- References
- 0
- Citations by year
- 2026: 534 · 2025: 702 · 2024: 706 · 2023: 719 · 2022: 657 · 2021: 991 · 2020: 989 · 2019: 1057 · 2018: 974 · 2017: 1026 · 2016: 1026 · 2015: 1120 · 2014: 1162 · 2013: 1172 · 2012: 1057
- FWCI
- 165.02
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Cambridge University Press eBooks (publishedVersion)
- CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Optical measurement and interference techniques
- Keywords
- multi-view geometry, projective geometry, fundamental matrix, trifocal tensor, scene reconstruction, photogrammetry, structure from motion, camera pose estimation, image correspondence
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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