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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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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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Experimental study of freely falling thin disks: Transition from planar zigzag to spiral
Zhong, H., Chen, S., & Lee, C. (2011) · Physics of Fluids · doi:10.1063/1.3541844 · Cited by 116
Zhang, Z., Huang, S., Meng, S., Gao, F., & Jiang, X. (2013) · Optics Express · doi:10.1364/oe.21.012218 · Cited by 115
Self-citationTelecentric stereo micro-vision system: Calibration method and experiments
Chen, Z., Liao, H., & Zhang, X. (2014) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2014.01.021 · Cited by 115
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Wang, Y. M., Li, Y., & Zheng, J. B. (2010) · International Conference on Information Sciences and Interaction Sciences · doi:10.1109/icicis.2010.5534797 · Cited by 115
Development of a prototype robot and fast path-planning algorithm for static laser weeding
Xiong, Y., Ge, Y., Liang, Y., & Blackmore, S. (2017) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2017.11.023 · Cited by 115
Key risk indicators for accident assessment conditioned on pre-crash vehicle trajectory
Shi, X., Wong, Y. D., Li, M. Z. F., & Chai, C. (2018) · Accident Analysis & Prevention · doi:10.1016/j.aap.2018.05.007 · Cited by 115
A survey on image and video stitching
Lyu, W., Zhou, Z., Chen, L., & Zhou, Y. (2019) · Virtual Reality & Intelligent Hardware · doi:10.3724/sp.j.2096-5796.2018.0008 · Cited by 121
Farahani, B. V., Barros, F., Sousa, P. J. S. C. P., Cacciari, P. P., Tavares, P. J., Futai, M. M., & Moreira, P. M. G. P. (2019) · Tunnelling and Underground Space Technology · doi:10.1016/j.tust.2019.102995 · Cited by 115
Gai, J., Xiang, L., & Tang, L. X. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106301 · Cited by 125
Song, K., Wang, J., Bao, Y., Huang, L., & Yan, Y. (2022) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2022.3215909 · Cited by 115
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Agrawal, & Davis (2003) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2003.1238428 · Cited by 115
Wang, W., Sakurada, K., & Kawaguchi, N. (2017) · Remote Sensing · doi:10.3390/rs9080851 · Cited by 116
Ren, P., Wang, J., Snyder, J., Tong, X., & Guo, B. (2011) · ACM Transactions on Graphics · doi:10.1145/2010324.1964940 · Cited by 114
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Marschner, S. R., Guenter, B. K., & Raghupathy, S. (2000) · Eurographics · doi:10.1007/978-3-7091-6303-0_21 · Cited by 114
Łuczyński, T., Pfingsthorn, M. F., & Birk, A. (2017) · Ocean Engineering · doi:10.1016/j.oceaneng.2017.01.029 · Cited by 113
Toward Holographic-Guided Surgery
Meulstee, J. W., Nijsink, H., Schreurs, R., Verhamme, L. M., Xi, T., Delye, H., Borstlap, W. A., & Maal, T. (2018) · Surgical Innovation · doi:10.1177/1553350618799552 · Cited by 113
A layered stereo algorithm using image segmentation and global visibility constraints
Bleyer, M., & Gelautz, M. (2005) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2004.1421743 · Cited by 113
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Schmalz, C., Forster, F., Schick, A., & Angelopoulou, E. (2012) · Medical Image Analysis · doi:10.1016/j.media.2012.04.001 · Cited by 112
Ismail, K., Sayed, T., & Saunier, N. (2013) · Canadian Journal of Civil Engineering · doi:10.1139/cjce-2011-0456 · Cited by 112
Visual Servoing of Nonholonomic Mobile Robots With Uncalibrated Camera-to-Robot Parameters
Zhang, X., Fang, Y., Li, B., & Wang, J. (2016) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2016.2598526 · Cited by 112
Immersive Mixed Reality for Manufacturing Training
González-Franco, M., Pizarro, R., Cermeron, J., Li, K., Thorn, J. T., Hutabarat, W., Tiwari, A., & Bermell-García, P. (2017) · Frontiers in Robotics and AI · doi:10.3389/frobt.2017.00003 · Cited by 135
Wang, Z., Kieu, H., Nguyen, H., & Le, M. (2014) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2014.04.002 · Cited by 112
Which pattern? Biasing aspects of planar calibration patterns and detection methods
Mallon, J., & Whelan, P. F. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2006.12.008 · Cited by 112
Bird’s-Eye View Vision System for Vehicle Surrounding Monitoring
Liu, J. Y.-C., Lin, K. K., & Chen, Y. (2008) · Springer eBooks · doi:10.1007/978-3-540-78157-8_16 · Cited by 111
A Perceptual Measure for Deep Single Image Camera Calibration
Hold-Geoffroy, Y., Sunkavalli, K., Eisenmann, J., Fisher, M., Gambaretto, E., Hadap, S., & Lalonde, J. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00250 · Cited by 111
Large-Scale Vision-Based Tactile Sensing for Robot Links: Design, Modeling, and Evaluation
Duong, L. V., & Ho, V. A. (2020) · IEEE Transactions on Robotics · doi:10.1109/tro.2020.3031251 · Cited by 118
Visual sensing technologies in robotic welding: Recent research developments and future interests
Xu, Y., & Wang, Z. (2021) · Sensors and Actuators A Physical · doi:10.1016/j.sna.2021.112551 · Cited by 111
Simple and fast rail wear measurement method based on structured light
Liu, Z., Sun, J., Wang, H., & Zhang, G. (2011) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2011.05.014 · Cited by 111
Fast Ego-motion Estimation with Multi-rate Fusion of Inertial and Vision
Armesto, L., Tornero, J., & Vincze, M. (2007) · The International Journal of Robotics Research · doi:10.1177/0278364907079283 · Cited by 111
Image Stitching based on Feature Extraction Techniques: A Survey
Adel, E., Elmogy, M. M., & El-Bakry, H. M. (2014) · International Journal of Computer Applications · doi:10.5120/17374-7818 · Cited by 111
Chen, M., Tang, Y., Zou, X., Huang, K. Y., Huang, Z., Zhou, H., Wang, C., & Lian, G. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105508 · Cited by 110
Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks
Ni, X., Luan, H., Kim, J.-T., Rogge, S. I., Bai, Y., Kwak, J. W., Liu, S., Yang, D. S., Li, S., Li, S., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31092-y · Cited by 110
A vision-based system for pre-inspection of steel frame manufacturing
Martı́nez, P., Ahmad, R., & Al‐Hussein, M. (2018) · Automation in Construction · doi:10.1016/j.autcon.2018.10.021 · Cited by 109
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Bartler, A., Mauch, L., Yang, B., Reuter, M., & Stoicescu, L. (2018) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2018.8553025 · Cited by 109
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Panadeiro, V., Rodríguez, Á., Henry, J., Włodkowic, D., & Andersson, M. J. (2021) · Lab Animal · doi:10.1038/s41684-021-00811-1 · Cited by 113
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Chow, J. K., Liu, K.-F., Tan, P. S., Su, Z., Wu, J., Li, Z., & Wang, Y.-H. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103959 · Cited by 113
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Ye, X., Song, F., Zhang, Z., & Qing-hua, Z. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3292427 · Cited by 120
Self-citationGledhill, D., Tian, G. Y., Taylor, D., & Clarke, D. (2003) · Computers & Graphics · doi:10.1016/s0097-8493(03)00038-4 · Cited by 109
Stamos, I., Liu, L., Chen, C., Wolberg, G., Yu, G. J., & Zokai, S. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-007-0089-1 · Cited by 109
Müller‐Linow, M., Pinto, F., Scharr, H., & Rascher, U. (2015) · Plant Methods · doi:10.1186/s13007-015-0052-z · Cited by 108
Li, B., An, Y., Cappelleri, D. J., Xu, J. L., & Zhang, S. (2017) · International Journal of Intelligent Robotics and Applications · doi:10.1007/s41315-016-0001-7 · Cited by 108
Hu, Y., Chen, Q., Feng, S., Tao, T., Asundi, A., & Zuo, C. (2018) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2018.09.011 · Cited by 108
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Luo, H., Yin, D., Zhang, S., Xiao, D., He, B., Meng, F., Zhang, Y., Cai, W., He, S., Zhang, W., et al. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.105099 · Cited by 108
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Müller, M., Rassweiler, M.-C., Klein, J. T., Seitel, A., Gondan, M., Baumhauer, M., Teber, D., Rassweiler, J. J., Meinzer, H.-P., & Maier‐Hein, L. (2013) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-013-0828-4 · Cited by 107
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Mountney, P., Stoyanov, D. V., Davison, A. J., & Yang, G. (2006) · Lecture notes in computer science · doi:10.1007/11866565_43 · Cited by 107
A usability study of multimodal input in an augmented reality environment
Lee, M.-K., Billinghurst, M., Baek, W., Green, R., & Woo, W. (2013) · Virtual Reality · doi:10.1007/s10055-013-0230-0 · Cited by 106
Bergen, T., & Wittenberg, T. C. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2384134 · Cited by 104
Voros, S., Long, J., & Cinquin, P. (2007) · The International Journal of Robotics Research · doi:10.1177/0278364907083395 · Cited by 106
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Moru, D. K., & Borro, D. (2019) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-019-04426-2 · Cited by 106
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Meyer, M., & Kuschk, G. (2019) · European Radar Conference (EuRAD) · Cited by 109
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Zhang, Z., Matsushita, Y., & Ma, Y. (2011) · doi:10.1109/cvpr.2011.5995548 · Cited by 105
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Qi, F., Han, J., Wang, P., Shi, G., & Li, F. (2012) · Pattern Recognition Letters · doi:10.1016/j.patrec.2012.06.003 · Cited by 106
Sani, M. F., & Karimian, G. (2017) · International Conference on Computer and Drone Applications (IConDA) · doi:10.1109/iconda.2017.8270408 · Cited by 105
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Zhang, B., Zhou, L., & Zhang, J. (2019) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12434 · Cited by 101
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Huang, L., Zhang, Q., & Asundi, A. (2013) · Optics Letters · doi:10.1364/ol.38.001446 · Cited by 100
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Jiang, G., & Quan, L. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.73 · Cited by 100
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Stoyanov, D. V. (2011) · Annals of Biomedical Engineering · doi:10.1007/s10439-011-0441-z · Cited by 99
Vision-Based Bridge Deformation Monitoring
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Deng, H. F., Hong, H., Luo, D., Deng, Y., & Su, C. (2019) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0001744 · Cited by 99
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High-Quality Depth from Uncalibrated Small Motion Clip
Ha, H., Im, S., Park, J., Jeon, H., & Kweon, I. S. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.584 · Cited by 97
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
About these checks
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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