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
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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]
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Genetic-Based Stereo Algorithm and Disparity Map Evaluation
Gong, M., & Yang, Y. (2002) · International Journal of Computer Vision · doi:10.1023/a:1014529404956 · Cited by 37
A New Algorithm for Energy Minimization with Discontinuities
Boykov, Y., Veksler, O., & Zabih, R. (1999) · Lecture notes in computer science · doi:10.1007/3-540-48432-9_15 · Cited by 33
A nonlinear diffusion model for discontinuous disparity and half-occlusions in stereo
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MAP-Based Stochastic Diffusion for Stereo Matching and Line Fields Estimation
Lee, S. H., Kanatsugu, Y., & Park, J.-I. (2002) · International Journal of Computer Vision · doi:10.1023/a:1014550009499 · Cited by 23
Linear image features in stereopsis
Kass, M. (1988) · International Journal of Computer Vision · doi:10.1007/bf00133572 · Cited by 21
Mean field theory for surface reconstruction
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Segmentation processes in visual perception: A cooperative neural model.
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Hierarchical stochastic diffusion for disparity estimation
Lee, S. H., Kanatsugu, Y., & Park, J.-I. (2002) · doi:10.1109/smbv.2001.988769 · Cited by 11
Solving Random-Dot Stereograms Using the Heat Equation
Szeliski, R. (2020) · Cited by 11
Self-citationGeneration of Temporally Consistent Multiple Virtual Camera Views from Stereoscopic Image Sequences
Shao, J. (2002) · International Journal of Computer Vision · doi:10.1023/a:1014545908590 · Cited by 7
Combination of stereo, motion and rendering for 3D footage display
Shao, J. (2002) · doi:10.1109/smbv.2001.988767 · Cited by 6
Design of Cooperative Networks
Marroquin, J. L. (1983) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 5
Flagged first, then by citations
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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
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Are we ready for autonomous driving? The KITTI vision benchmark suite
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The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
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An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision
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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 ›
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