The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation
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
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
Self-citationSLIC Superpixels Compared to State-of-the-Art Superpixel Methods
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Süsstrunk, S. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.120 · Cited by 9,201
Shape and motion from image streams under orthography: a factorization method
Tomasi, C., & Kanade, T. (1992) · International Journal of Computer Vision · doi:10.1007/bf00129684 · Cited by 2,878
Photo tourism: exploring photo collections in 3D
Snavely, N., Seitz, S. M., & Szeliski, R. (2006) · ACM Transactions on Graphics · doi:10.1145/1141911.1141964 · Cited by 2,866
Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information
Hirschmuller, H. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.56 · Cited by 1,698
DeepFlow: Large Displacement Optical Flow with Deep Matching
Weinzaepfel, P., Revaud, J., Harchaoui, Z., & Schmid, C. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.175 · Cited by 997
ClassCut for Unsupervised Class Segmentation
Alexe, B., Deselaers, T., & Ferrari, V. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15555-0_28 · Cited by 993
Segment-Based Stereo Matching Using Belief Propagation and a Self-Adapting Dissimilarity Measure
Klaus, A., Sormann, M., & Karner, K. (2006) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2006.1033 · Cited by 837
A Multibody Factorization Method for Independently Moving Objects
Costeira, J. P., & Kanade, T. (1998) · International Journal of Computer Vision · doi:10.1023/a:1008000628999 · Cited by 661
Cited by
Object scene flow for autonomous vehicles
Menze, M., & Geiger, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298925 · Cited by 2,270
Wang, Y., Chao, W.-L., Garg, D., Hariharan, B., Campbell, M., & Weinberger, K. Q. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00864 · Cited by 1,154
A Multi-view Stereo Benchmark with High-Resolution Images and Multi-camera Videos
Schops, T., Schonberger, J. L., Galliani, S., Sattler, T., Schindler, K., Pollefeys, M., & Geiger, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.272 · Cited by 915
(2015) · Cited by 656
Superpixels: An evaluation of the state-of-the-art
Stutz, D., Hermans, A., & Leibe, B. (2018) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2017.03.007 · Cited by 538
Metadata
- Authors
- Yamaguchi, K., McAllester, D., & Urtasun, R.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2014
- Pages
- 756-771
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W55377555
- Cited by
- 297
- References
- 32
- FWCI
- 21.80
- 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: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. https://app.cochat.ai/papers/10.1007/978-3-319-10602-1_49
- The paper
- Yamaguchi, K., McAllester, D., & Urtasun, R. (2014). Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-319-10602-1_49
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.
