The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
3D Scene Flow Estimation with a Piecewise Rigid Scene Model
Open accessOverview
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
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
A Database and Evaluation Methodology for Optical Flow
Baker, S., Scharstein, D., Lewis, J. P., Roth, S., Black, M. J., & Szeliski, R. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-010-0390-2 · Cited by 2,205 · cited 1×
“While it may not be surprising that considering longer sequences may help motionestimation,atleastinclassical2Dopticalflowestima-tion multi-frame extensions have largely not had the desired effect; two-frame methods are still the state of the art (see Baker et al. 2011; Geiger et al. 2012).”
BackgroundSelf-citationFusionFlow: Discrete-continuous optimization for optical flow estimation
Lempitsky, V., Roth, S., & Rother, C. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587751 · Cited by 145 · cited 2×
“(11), the pseudo-Boolean function (Lempitsky et al. 2008) representing the local energy has at most ( 2 N S − 1 ) 2 variables, which makes the optimization efficient.”
BackgroundSelf-citation
Cited by
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
Mayer, N., Ilg, E., Hausser, P., Fischer, P., Cremers, D., Dosovitskiy, A., & Brox, T. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.438 · Cited by 2,488
GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose
Yin, Z., & Shi, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00212 · Cited by 1,256
Large scale deep learning for computer aided detection of mammographic lesions
Kooi, T., Litjens, G., van Ginneken, B., Gubern-Mérida, A., Sánchez, C. I., Mann, R., den Heeten, A., & Karssemeijer, N. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.07.007 · Cited by 965
FlowNet3D: Learning Scene Flow in 3D Point Clouds
Liu, X., Qi, C. R., & Guibas, L. J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00062 · Cited by 499
Metadata
- Authors
- Vogel, C., Schindler, K., & Roth, S.
- Venue
- International Journal of Computer Vision
- Published
- 2015-02-24
- Volume
- 115
- Issue
- 1
- Pages
- 1-28
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W1993850091
- Cited by
- 209
- References
- 3
- FWCI
- 25.42
- Type
- journal article
- Language
- en
- Open access
- green
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: 3D Scene Flow Estimation with a Piecewise Rigid Scene Model. https://app.cochat.ai/papers/10.1007/s11263-015-0806-0
- The paper
- Vogel, C., Schindler, K., & Roth, S. (2015). 3D Scene Flow Estimation with a Piecewise Rigid Scene Model. International Journal of Computer Vision. https://doi.org/10.1007/s11263-015-0806-0
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.
