Unifying Image Plane Liftings for Central Catadioptric and Dioptric Cameras
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Visual SLAM with LSD SLAM achieves a Relocalization (%) of 61 on the WoodScape dataset.
“| Visual SLAM | LSD SLAM [15] | Relocalization (%) | 61 |”
From WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving (2019) · cites this paper as [15]
Cites
Decentering Distortion of Lenses
Brown, D. C. (1966) · Photogrammetric Engineering & Remote Sensing · Cited by 712 · see citing works
Algebraic Projective Geometry.
Atchison, W. F., Semple, J. G., & Kneebone, G. T. (1953) · American Mathematical Monthly · doi:10.2307/2307420 · Cited by 708 · see citing works
A Unifying Theory for Central Panoramic Systems and Practical Implications
Geyer, C., & Daniilidis, K. (2000) · Lecture notes in computer science · doi:10.1007/3-540-45053-x_29 · Cited by 510 · see citing works
A theory of catadioptric image formation
Baker, S. J., & Nayar, S. K. (2002) · International Conference on Computer Vision · doi:10.1109/iccv.1998.710698 · Cited by 380 · see citing works
Elementary mathematics from an advanced standpoint
(1954) · Journal of the Franklin Institute · doi:10.1016/0016-0032(54)91081-9 · Cited by 289 · see citing works
Epipolar Geometry for Central Catadioptric Cameras
Svoboda, T., & Pajdla, T. (2002) · International Journal of Computer Vision · doi:10.1023/a:1019869530073 · Cited by 197 · see citing works
Geometric properties of central catadioptric line images and their application in calibration
Barreto, J. P., & Araújo, H. J. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.163 · Cited by 182 · see citing works
Self-citation(1991) · Elsevier eBooks · doi:10.1016/c2013-0-11551-6 · Cited by 165 · see citing works
Generalized principal component analysis (GPCA)
Vidal, R., Ma, Y., & Sastry, S. S. (2003) · 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.2003.1211411 · Cited by 142 · see citing works
Mirrors in motion: epipolar geometry and motion estimation
Geyer, C., & Daniilidis, K. (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238426 · Cited by 95 · see citing works
Mixing catadioptric and perspective cameras
Sturm, P. F. (2003) · doi:10.1109/omnvis.2002.1044489 · Cited by 74 · see citing works
What is the center of the image?
Willson, R. G., & Shafer, S. A. (2002) · 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.1993.341035 · Cited by 65 · see citing works
On the epipolar geometry of the Crossed-Slits projection
Feldman, Pajdla, & Weinshall (2003) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2003.1238456 · Cited by 48 · see citing works
General central projection systems : modeling, calibration and visual servoing
Barreto, J. P. D. A. (2004) · Cited by 47 · see citing works
Self-citationParacatadioptric camera calibration using lines
Barreto, J. P., & Araújo, H. J. (2003) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2003.1238648 · Cited by 35 · see citing works
Self-citationOn Projection Matrices and their Applications in Computer Vision.
Wolf, L., & Shashua, A. (2001) · Cited by 26 · see citing works
Direct Least Square Fitting of Paracatadioptric Line Images
Barreto, J. P., & Araújo, H. J. (2003) · IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2003.10083 · Cited by 26 · see citing works
Self-citationCatadioptric camera calibration using geometric invariants
Ying, X., & Hu, Z. (2003) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2003.1238647 · Cited by 19 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving
Yogamani, S., Hughes, C., Horgan, J., Sistu, G., Chennupati, S., Uricar, M., Milz, S., Simon, M., Amende, K., Witt, C., et al. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00940 · Cited by 303 · see citing works
“As a comparison, to give the reader an understanding of how different models behave, Figure 3 shows the mapping function r(θ) for five different projection models, which are Polynomial, Rectilinear, Stereographic, UCM and eUCM.”
MethodLarge-scale direct SLAM for omnidirectional cameras
Caruso, D., Engel, J., & Cremers, D. (2015) · 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353366 · Cited by 244 · see citing works
- Authors
- Barreto, J. P.
- Affiliations
- University of Coimbra (PT)
- Corresponding author
- Barreto, J. P.
- ORCID
- Barreto, J. P.
- Venue
- Imaging Beyond the Pinhole Camera
- Publisher
- Springer Netherlands
- Published
- 2006-09-20
- Pages
- 21-38
- OpenAlex
- W172887045
- Cited by
- 26
- References
- 18
- Citations by year
- 2024: 2 · 2021: 2 · 2020: 1 · 2019: 1 · 2017: 2 · 2015: 2 · 2013: 1
- FWCI
- 7.50
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Springer eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Optical measurement and interference techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Object Detection Techniques
- Keywords
- catadioptric cameras, Veronese map, radial distortion, collineations, hyperplanes, mirrors and lenses
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
