The DOI matches its Crossref record.
In defence of the 8-point algorithm
Abstract
The fundamental matrix is a basic tool in the analysis of scenes taken with two uncalibrated cameras, and the 8 point algorithm is a frequently cited method for computing the fundamental matrix from a set of 8 or more point matches. It has the advantage of simplicity of implementation. The prevailing view is, however, that it is extremely susceptible to noise and hence virtually useless for most purposes. The paper challenges that view, by showing that by preceding the algorithm with a very simple normalization (translation and scaling) of the coordinates of the matched points, results are obtained comparable with the best iterative algorithms. This improved performance is justified by theory and verified by extensive experiments on real images.>
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 11
- golub, 1989, Matrix Computations
- faugeras, 1992, Computer Vision - ECCV '92 LNCS-Series, 588, 563, 10.1007/3-540-55426-2_61 · DOI · Paper page
- press, 1988, Numerical Recipes in C The Art of Scientific Computing
- hartley, 1993, Euclidean reconstruction from uncalibrated views, Proc of the Second Europe-US Workshop on Invariance, 187
- 10.1109/TPAMI.1984.4767471 · DOI · Paper page
- 10.1109/CVPR.1992.223179 · DOI
- luong, 1993, On determining the fundamental matrix Analysis of different methods and experimental results
- 10.1038/293133a0 · DOI · Paper page
- deriche, 1994, Computer Vision - ECCV '94 Volume I LNCS-Series, 800, 567, 10.1007/3-540-57956-7_64 · DOI
- 10.1098/rsta.1990.0099 · doi:10.1098/rsta.1990.0099
- 10.1007/BFb0028337 · DOI
Cited by
A flexible new technique for camera calibration
Zhang, Z. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.888718 · Cited by 15,004
Robust Wide Baseline Stereo from Maximally Stable Extremal Regions
Matas, J., Chum, O., Urban, M., & Pajdla, T. (2002) · Procedings of the British Machine Vision Conference 2002 · doi:10.5244/c.16.36 · Cited by 1,350
A Flexible Technique for Accurate Omnidirectional Camera Calibration and Structure from Motion
Scaramuzza, D., Martinelli, A., & Siegwart, R. (2006) · Fourth IEEE International Conference on Computer Vision Systems (ICVS'06) · doi:10.1109/icvs.2006.3 · Cited by 417
Visual Odometry Revisited: What Should Be Learnt?
Zhan, H., Weerasekera, C. S., Bian, J.-W., & Reid, I. (2020) · 2020 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra40945.2020.9197374 · Cited by 201
Combining two-view constraints for motion estimation
Govindu, V. M. (2005) · Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 · doi:10.1109/cvpr.2001.990963 · Cited by 196
Metadata
- Authors
- Hartley, R. I.
- Venue
- Proceedings of IEEE International Conference on Computer Vision
- Publisher
- IEEE Comput. Soc. Press
- Published
- 2002-11-19
- Pages
- 1064-1070
- OpenAlex
- W2138620157
- Cited by
- 649
- References
- 0
- FWCI
- 36.20
- Type
- conference paper
- Language
- en
- Open access
- closed
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: In defence of the 8-point algorithm. https://app.cochat.ai/papers/10.1109/iccv.1995.466816
- The paper
- Hartley, R. I. (2002). In defence of the 8-point algorithm. Proceedings of IEEE International Conference on Computer Vision. https://doi.org/10.1109/iccv.1995.466816
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
