The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
SBA: A software package for generic sparse bundle adjustment
Abstract
Bundle adjustment constitutes a large, nonlinear least-squares problem that is often solved as the last step of feature-based structure and motion estimation computer vision algorithms to obtain optimal estimates. Due to the very large number of parameters involved, a general purpose least-squares algorithm incurs high computational and memory storage costs when applied to bundle adjustment. Fortunately, the lack of interaction among certain subgroups of parameters results in the corresponding Jacobian being sparse, a fact that can be exploited to achieve considerable computational savings. This article presents sba, a publicly available C/C++ software package for realizing generic bundle adjustment with high efficiency and flexibility regarding parameterization.
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
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
An Algorithm for Least-Squares Estimation of Nonlinear Parameters
Marquardt, D. W. (1963) · Journal of the Society for Industrial and Applied Mathematics · doi:10.1137/0111030 · Cited by 30,728
Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
(2021) · A First Course in Statistical Programming with R · doi:10.1017/9781108993456.011 · Cited by 13,724
A method for the solution of certain non-linear problems in least squares
Levenberg, K. (1944) · Quarterly of Applied Mathematics · doi:10.1090/qam/10666 · Cited by 12,359
(1999) · Springer Series in Operations Research and Financial Engineering · doi:10.1007/b98874 · Cited by 9,482
Shi, J., & Tomasi (1994) · Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94 · doi:10.1109/cvpr.1994.323794 · Cited by 7,724
Numerical Methods for Unconstrained Optimization and Nonlinear Equations
Dennis, J. E., & Schnabel, R. B. (1996) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611971200 · Cited by 7,615
Golub, G. H., & Loan, C. F. V. (1996) · Johns Hopkins University Press eBooks · Cited by 6,027
The Levenberg-Marquardt algorithm: Implementation and theory
Moré, J. J. (1978) · Lecture Notes in Mathematics · doi:10.1007/bfb0067700 · Cited by 5,773
Cited by
Structure-from-Motion Revisited
Schonberger, J. L., & Frahm, J.-M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.445 · Cited by 6,433
RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments
Henry, P., Krainin, M., Herbst, E., Ren, X., & Fox, D. (2012) · The International Journal of Robotics Research · doi:10.1177/0278364911434148 · Cited by 1,161
Discovering and exploiting 3D symmetries in structure from motion
Cohen, A., Zach, C., Sinha, S. N., & Pollefeys, M. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247841 · Cited by 67
Light-weight localization for vehicles using road markings
Ranganathan, A., Ilstrup, D., & Wu, T. (2013) · 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2013.6696460 · Cited by 50
Metadata
- Authors
- Lourakis, M. I. A., & Argyros, A. A.
- Venue
- ACM Transactions on Mathematical Software
- Published
- 2009-03
- Volume
- 36
- Issue
- 1
- Pages
- 1-30
- ISSN
- 0098-3500 (ISSN-L)
- 1557-7295
- OpenAlex
- W2133192850
- Cited by
- 788
- References
- 54
- FWCI
- 69.26
- Type
- journal article
- 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: SBA: A software package for generic sparse bundle adjustment. https://app.cochat.ai/papers/10.1145/1486525.1486527
- The paper
- Lourakis, M. I. A., & Argyros, A. A. (2009). SBA: A software package for generic sparse bundle adjustment. ACM Transactions on Mathematical Software. https://doi.org/10.1145/1486525.1486527
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.
