The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Non-iterative approach for fast and accurate vanishing point detection
Abstract
We present an algorithm that quickly and accurately estimates vanishing points in images of man-made environments. Contrary to previously proposed solutions, ours is neither iterative nor relies on voting in the space of vanishing points. Our formulation is based on a recently proposed algorithm for the simultaneous estimation of multiple models called J-Linkage. Our method avoids representing edges on the Gaussian sphere and the computations and error measures are done in the image. We show that a consistency measure between a vanishing point and an edge of the image can be computed in closed-form while being geometrically meaningful. Finally, given a set of estimated vanishing points, we show how this consistency measure can be used to identify the three vanishing points corresponding to the Manhattan directions. We compare our algorithm with other approaches on the York Urban Database and show significant performance improvements.
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
Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
Mean shift: a robust approach toward feature space analysis
Comaniciu, D., & Meer, P. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.1000236 · Cited by 11,305
Generalized principal component analysis (GPCA)
Vidal, R., Ma, Y., & Sastry, S. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.244 · Cited by 1,038
Using vanishing points for camera calibration
Caprile, B., & Torre, V. (1990) · International Journal of Computer Vision · doi:10.1007/bf00127813 · Cited by 711
Manhattan World: compass direction from a single image by Bayesian inference
Coughlan, J. M., & Yuille, A. L. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790349 · Cited by 475
Robust Multiple Structures Estimation with J-Linkage
Toldo, R., & Fusiello, A. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88682-2_41 · Cited by 393
Interpreting perspective images
Barnard, S. T. (1983) · Artificial Intelligence · doi:10.1016/s0004-3702(83)80021-6 · Cited by 357
Košecká, J., & Zhang, W. (2002) · Lecture Notes in Computer Science · doi:10.1007/3-540-47979-1_32 · Cited by 293
The multiRANSAC algorithm and its application to detect planar homographies
Zuliani, M., Kenney, C. S., & Manjunath, B. S. (2005) · IEEE International Conference on Image Processing 2005 · doi:10.1109/icip.2005.1530351 · Cited by 288
A new approach to vanishing point detection in architectural environments
Rother, C. (2002) · Image and Vision Computing · doi:10.1016/s0262-8856(02)00054-9 · Cited by 255
Cited by
StructSLAM: Visual SLAM With Building Structure Lines
Zhou, H., Zou, D., Pei, L., Ying, R., Liu, P., & Yu, W. (2015) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2015.2388780 · Cited by 280
Bell (2013) · doi:10.1145/2461912.2462002 · Cited by 200
Metadata
- Authors
- Tardif, J.-P.
- Venue
- 2009 IEEE 12th International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2009-09
- Pages
- 1250-1257
- OpenAlex
- W2539923485
- Cited by
- 264
- References
- 16
- FWCI
- 11.18
- 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: Non-iterative approach for fast and accurate vanishing point detection. https://app.cochat.ai/papers/10.1109/iccv.2009.5459328
- The paper
- Tardif, J.-P. (2009). Non-iterative approach for fast and accurate vanishing point detection. 2009 IEEE 12th International Conference on Computer Vision. https://doi.org/10.1109/iccv.2009.5459328
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.
