The DOI matches its Crossref record; 29 of the 30 references it cites were found in a registry or an index.
A flexible new technique for camera calibration
Abstract
We propose a flexible technique to easily calibrate a camera. It only requires the camera to observe a planar pattern shown at a few (at least two) different orientations. Either the camera or the planar pattern can be freely moved. The motion need not be known. Radial lens distortion is modeled. The proposed procedure consists of a closed-form solution, followed by a nonlinear refinement based on the maximum likelihood criterion. Both computer simulation and real data have been used to test the proposed technique and very good results have been obtained. Compared with classical techniques which use expensive equipment such as two or three orthogonal planes, the proposed technique is easy to use and flexible. It advances 3D computer vision one more step from laboratory environments to real world use.
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“The widely used calibration method is proposed by Z. Zhang in [156] based on planar patterns at a few different orientations, where radial lens distortion is modelled.”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [156]
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Cites
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
Tsai, R. (1987) · IEEE Journal on Robotics and Automation · doi:10.1109/jra.1987.1087109 · Cited by 5,890
The Levenberg-Marquardt algorithm: Implementation and theory
Morè, J. J. (1978) · Lecture notes in mathematics · doi:10.1007/bfb0067700 · Cited by 5,796
Three-dimensional computer vision: a geometric viewpoint
(1994) · Choice Reviews Online · doi:10.5860/choice.32-0357 · Cited by 3,032
Camera calibration with distortion models and accuracy evaluation
Weng, J., Cohen, P. R., & Herniou, M. (1992) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.159901 · Cited by 1,767
Camera self-calibration: Theory and experiments
Faugeras, O. D., Luong, Q.-T., & Maybank, S. J. (1992) · Lecture notes in computer science · doi:10.1007/3-540-55426-2_37 · Cited by 957
Slama, C. C., Theurer, C. B., & Henriksen, S. W. (1980) · American Society of Photogrammetry eBooks · Cited by 856
A theory of self-calibration of a moving camera
Maybank, S. J., & Faugeras, O. D. (1992) · International Journal of Computer Vision · doi:10.1007/bf00127171 · Cited by 831
Using vanishing points for camera calibration
Caprile, B., & Torre, V. (1990) · International Journal of Computer Vision · doi:10.1007/bf00127813 · Cited by 715
Algebraic Projective Geometry.
Atchison, W. F., Semple, J. G., & Kneebone, G. T. (1953) · American Mathematical Monthly · doi:10.2307/2307420 · Cited by 708
Flagged first, then by citations
1 of the 30 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- brown, 1971, close-range camera calibration, Photogrammetric Eng, 37, 855
- faugeras, 1986, the calibration problem for stereo, Proc IEEE Conf Computer Vision and Pattern Recognition, 15
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Jawaid, M., Elms, E., Latif, Y., & Chin, T.-J. (2023) · 2023 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48891.2023.10160531 · Cited by 32
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Shao, X., Zhang, L., Zhang, T., Shen, Y., Li, H., & Zhou, Y. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413867 · Cited by 23
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Zhang, X., Zhu, Y., Li, C., Zhao, J., & Li, G. (2014) · Bio-Medical Materials and Engineering · doi:10.3233/bme-141103 · Cited by 8
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Metadata
- Authors
- Zhang, Z.
- Affiliations
- Microsoft (United States) (US)
- Corresponding author
- Zhang, Z.
- ORCID
- Zhang, Z.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2000
- Volume
- 22
- Issue
- 11
- Pages
- 1330-1334
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2167667767
- Cited by
- 15,004
- References
- 30
- Citations by year
- 2026: 858 · 2025: 1043 · 2024: 1031 · 2023: 1035 · 2022: 941 · 2021: 898 · 2020: 840 · 2019: 897 · 2018: 814 · 2017: 771 · 2016: 707 · 2015: 650 · 2014: 595 · 2013: 616 · 2012: 502
- FWCI
- 79.80
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Optical measurement and interference techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Object Detection Techniques
- Keywords
- camera calibration, planar pattern, radial distortion, analytical solution, maximum likelihood refinement, 3D vision
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