On Benchmarking Optical Flow
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The resulting FlyingChairs images resemble those from McCane et al (2001), but with thousands of randomized scenes instead of a few manually created ones.
“The resulting images resemble those from McCane et al (2001) ( cf. Fig. 1), yet with the crucial difference that there are thousands of randomized scenes instead of a few manually created ones.”
From What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation? (2018) · cites this paper as McCane et al (2001)
Cites
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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 · see citing works
“Most recently McCane et al. [12] provided more ground truth for real polyhedral scenes as well as graphics scenes of varying realism.”
BackgroundA Naturalistic Open Source Movie for Optical Flow Evaluation
Butler, D. J., Wulff, J., Stanley, G. B., & Black, M. J. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33783-3_44 · Cited by 1,842 · see citing works
What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
Mayer, N., Ilg, E., Fischer, P., Hazirbas, C., Cremers, D., Dosovitskiy, A., & Brox, T. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1082-6 · Cited by 191 · see citing works
- Authors
- McCane, B., Novins, K., Crannitch, D., & Galvin, B.
- Affiliations
- University of Auckland (NZ)
- University of Otago (NZ)
- Venue
- Computer Vision and Image Understanding
- Publisher
- Elsevier BV
- Published
- 2001-10
- Volume
- 84
- Issue
- 1
- Pages
- 126-143
- ISSN
- 1077-3142 (ISSN-L)
- OpenAlex
- W2145973785
- Cited by
- 250
- References
- 19
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 4 · 2022: 4 · 2021: 4 · 2020: 5 · 2019: 2 · 2018: 5 · 2017: 6 · 2016: 17 · 2015: 7 · 2014: 19 · 2013: 13 · 2012: 22
- FWCI
- 8.22
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computer Vision and Image Understanding (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Signal Processing › Video Coding and Compression Technologies
- Keywords
- optical flow, benchmarking, ground truth, polyhedra, quantitative evaluation, test suite
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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.
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