The DOI matches its Crossref record.
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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“Accurate real-time pose estimation without iterative refinement would be required to power self-driving image-guidance systems [45].”
From Rapid patient-specific neural networks for intraoperative X-ray to volume registration (2025) · cites this paper as [45]
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“Within the calibration process the internal parameters of each camera as well as the relative orientation among the cameras were determined [20].”
From Diving-Flight Aerodynamics of a Peregrine Falcon (Falco peregrinus) (2014) · cites this paper as [20]
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Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
Scharstein, D., & Szeliski, R. (2002) · International Journal of Computer Vision · doi:10.1023/a:1014573219977 · Cited by 6,740
ORB-SLAM: A Versatile and Accurate Monocular SLAM System
Mur-Artal, R., Montiel, J. M. M., & Tardos, J. D. (2015) · IEEE Transactions on Robotics · doi:10.1109/tro.2015.2463671 · Cited by 6,778
A performance evaluation of local descriptors
Mikolajczyk, K., & Schmid, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.188 · Cited by 6,723
Unscented Filtering and Nonlinear Estimation
Julier, S., & Uhlmann, J. K. (2004) · Proceedings of the IEEE · doi:10.1109/jproc.2003.823141 · Cited by 6,798
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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