The DOI matches its Crossref record; 141 of the 141 references it cites were found in a registry or an index.
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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?
In the citing paper
“The presence of nonrigid or independent motion makes collecting a ground-truth dataset for optical flow far harder than for stereo, say, where structured light (Scharstein and Szeliski 2002) or range scanning (Seitz et al. 2006) can be used to obtain ground truth.”
From A Database and Evaluation Methodology for Optical Flow (2011) · cites this paper as Scharstein and Szeliski 2002
In the citing paper
“We follow the procedure in Scharstein and Szeliski (2002) and compute the error measure statistics over three types of region masks: everywhere ( All ), around motion discontinuities ( Disc ), and in textureless regions ( Untext ).”
From A Database and Evaluation Methodology for Optical Flow (2011) · cites this paper as Scharstein and Szeliski (2002)
In the citing paper
“We also compute the robustness statistics used in the Middlebury stereo dataset (Scharstein and Szeliski 2002).”
From A Database and Evaluation Methodology for Optical Flow (2011) · cites this paper as Scharstein and Szeliski 2002
In the citing paper
“The most appropriate performance statistics for this data, therefore, are the robustness statistics used in the Middlebury stereo dataset (Scharstein and Szeliski 2002) (Sect. 4.2).”
From A Database and Evaluation Methodology for Optical Flow (2011) · cites this paper as Scharstein and Szeliski 2002
In the citing paper
“We achieve high accuracy despite using a lower resolution than [47]'s 1024 × 384, with substantially less training time (20 vs. 200 epochs) and no use of postprocessing.”
From Digging Into Self-Supervised Monocular Depth Estimation (2019) · cites this paper as [47]
In the citing paper
“We see that higher resolution helps, confirming the finding in [47].”
From Digging Into Self-Supervised Monocular Depth Estimation (2019) · cites this paper as [47]
In the citing paper
“While Superdepth [47] comes a close second to our algorithm in the S category, they are run at high resolution (1024 × 384 vs. our 640 × 192), and in Table 1 we show that at higher resolutions our model's performance also increases.”
From Digging Into Self-Supervised Monocular Depth Estimation (2019) · cites this paper as [47]
Citations
Cites
A Computational Approach to Edge Detection
Canny, J. (1986) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1986.4767851 · Cited by 30,004
Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949
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
(1981) · Cited by 11,963
Fast approximate energy minimization via graph cuts
Boykov, Y., Veksler, O., & Zabih, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969114 · Cited by 6,988
The Laplacian pyramid as a compact image code
Burt, P., & Adelson, E. (1983) · IEEE Transactions on Communications · doi:10.1109/tcom.1983.1095851 · Cited by 6,077
Marr, D. (1982) · Cumulative Index of Computer Aided Architectural Design · Cited by 5,733
An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision
Boykov, Y., & Kolmogorov, V. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.60 · Cited by 4,577
Performance of optical flow techniques
Barron, J. L., Fleet, D. J., & Beauchemin, S. S. (1994) · International Journal of Computer Vision · doi:10.1007/bf01420984 · Cited by 4,305
Flagged first, then by citations
Research works printed but not linked: 11
- Baker, H.H. 1980. Edge based stereo correlation. In Image Understanding Workshop, L.S. Baumann (Ed.). Science Applications International Corporation, pp. 168–175.
- Bolles, R.C., Baker, H.H., and Hannah, M.J. 1993. The JISCT stereo evaluation. In DARPA Image Understanding Workshop, pp. 263–274.
- Hartley, R.I. and Zisserman, A. 2000. Multiple Views Geometry. Cambridge University Press: Cambridge, UK.
- Kanade, T. 1994. Development of a videorate stereo machine. In Image Understanding Workshop, Monterey, CA, 1994. Morgan Kaufmann Publishers: San Mateo, CA, pp. 549–557.
- Kang, S.B., Webb, J., Zitnick, L., and Kanade, T. 1995. A multibase-line stereo system with active illumination and realtime image acquisition. In ICCV, pp. 88–93.
- Marr, D. 1982. Vision. Freeman: New York.
- Mulligan, J., Isler, V., and Danulidis, K. 2001. Performance evaluation of stereo for telepresence. In ICCV, Vol. II, pp. 558–565.
- Scharstein, D., Szeliski, R., and Zabih, R. 2001. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. In IEEE Workshop on Stereo and Multi-Baseline Vision.
- Seitz, P. 1989. Using local orientation information as image primitive for robust object recognition. In SPIE Visual Communications and Image Processing IV, Vol. 1199, pp. 1630–1639.
- Tao, H., Sawhney, H., and Kumar, R. 2001. Aglobal matching frame-work for stereo computation. In ICCV, Vol. I, pp. 532–539.
- Zhang, Z. 1998. Determining the epipolar geometry and its uncertainty: A review. IJCV, 27(2):161–195. · DOI
Cited by
Sorted by citations
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00068 · Cited by 14,623
An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision
Boykov, Y., & Kolmogorov, V. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.60 · Cited by 4,577
Metadata
- Authors
- Scharstein, D., & Szeliski, R.
- Affiliations
- Middlebury College (US)
- Venue
- International Journal of Computer Vision
- Published
- 2002-04
- Volume
- 47
- Issue
- 1-3
- Pages
- 7-42
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2104974755
- Cited by
- 6,740
- References
- 141
- Citations by year
- 2026: 119 · 2025: 128 · 2024: 147 · 2023: 145 · 2022: 171 · 2021: 293 · 2020: 297 · 2019: 313 · 2018: 316 · 2017: 343 · 2016: 350 · 2015: 419 · 2014: 444 · 2013: 407 · 2012: 507
- FWCI
- 143.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Journal of Computer Vision (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Optical measurement and interference techniques
- Keywords
- stereo matching, dense stereo matching, algorithm taxonomy
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
