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Three-dimensional scene flow
Abstract
Just as optical flow is the two-dimensional motion of points in an image, scene flow is the three-dimensional motion of points in the world. The fundamental difficulty with optical flow is that only the normal flow can be computed directly from the image measurements, without some form of smoothing or regularization. In this paper, we begin by showing that the same fundamental limitation applies to scene flow; however, many cameras are used to image the scene. There are then two choices when computing scene flow: 1) perform the regularization in the images or 2) perform the regularization on the surface of the object in the scene. In this paper, we choose to compute scene flow using regularization in the images. We describe three algorithms, the first two for computing scene flow from optical flows and the third for constraining scene tructure from the inconsistencies in multiple optical flows.
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Research works printed but not linked: 23
- 10.7551/mitpress/2002.003.0004 · DOI · Paper page
- Avidan, Nonrigid Parallax for 3D Linear Motion, 2, 62
- 10.1007/BF01420984 · DOI · Paper page
- 10.1109/ICCV.1993.378214 · DOI · Paper page
- 10.1109/ICCV.2001.937603 · DOI
- 10.1023/A:1008000628999 · DOI · Paper page
- Horn, 1986, Robot Vision
- 10.1007/s001380050038 · doi:10.1007/s001380050038
- Lucas, An Iterative Image Registration Technique with an Application to Stereo Vision, 674
- 10.1006/cviu.1996.0481 · doi:10.1006/cviu.1996.0481
- 10.1109/34.216727 · DOI · Paper page
- 10.1109/TPAMI.1987.4767884 · DOI
- 10.1006/cviu.1994.1048 · doi:10.1006/cviu.1994.1048
- 10.1109/34.85661 · DOI · Paper page
- 10.1023/A:1008176507526 · DOI · Paper page
- 10.1364/JOSA.57.001105 · DOI · Paper page
- 10.7551/mitpress/3877.001.0001 · DOI · Paper page
- 10.1068/p130255 · DOI · Paper page
- 10.1145/1061347.1061351 · DOI
- Vedula, Spatio-Temporal View Interpolation, 65
- 10.1109/ICCV.1999.790293 · DOI · Paper page
- 10.1109/TPAMI.1986.4767853 · DOI · Paper page
- 10.1109/34.57666 · doi:10.1109/34.57666
Cited by
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
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
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
A Database and Evaluation Methodology for Optical Flow
Baker, S., Roth, S., Scharstein, D., Black, M. J., Lewis, J. P., & Szeliski, R. (2007) · 2007 IEEE 11th International Conference on Computer Vision · doi:10.1109/iccv.2007.4408903 · Cited by 618
Learning Motion Patterns in Videos
Tokmakov, P., Alahari, K., & Schmid, C. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.64 · Cited by 267
Metadata
- Authors
- Vedula, S., Rander, P., Collins, R., & Kanade, T.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2005-03
- Volume
- 27
- Issue
- 3
- Pages
- 475-480
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2129671742
- Cited by
- 281
- References
- 0
- FWCI
- 15.96
- Type
- journal article
- Language
- en
- Open access
- closed
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- Vedula, S., Rander, P., Collins, R., & Kanade, T. (2005). Three-dimensional scene flow. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2005.63
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