Full flow: Optical flow estimation by global optimization over regular grids

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 2016-December, Issue , 2016, Pages 4706-4714

  Chen, Qifeng ^a   Koltun, Vladlen ^b  

^a STANFORD UNIVERSITY   (United States)

^b INTEL CORPORATION   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; COMPUTATIONAL COMPLEXITY; COMPUTATIONAL EFFICIENCY; COMPUTER VISION; GLOBAL OPTIMIZATION; MAPPING; OPTICAL FLOWS;

CONTINUOUS INTERPOLATIONS; DESCRIPTOR MATCHING; INNER LOOPS; OPTICAL FLOW ESTIMATION; OPTIMIZATION APPROACH; REGULAR GRIDS; REGULAR STRUCTURE; STATE-OF-THE-ART PERFORMANCE;

PATTERN RECOGNITION;

EID: 84986294573     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.509     Document Type: Conference Paper

References (47)

1. A. Aggarwal, M. M. Klawe, S. Moran, P. W. Shor, and R. E. Wilber. Geometric applications of a matrix-searching algorithm. Algorithmica, 2, 1987.
2. C. Bailer, B. Taetz, and D. Stricker. Flow fields: Dense correspondence fields for highly accurate large displacement optical flow estimation. In ICCV, 2015.
3. S. Baker, D. Scharstein, J. P. Lewis, S. Roth, M. J. Black, and R. Szeliski. A database and evaluation methodology for optical flow. IJCV, 92(1), 2011.
4. M. J. Black and P. Anandan. The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields. Computer Vision and Image Understanding, 63(1), 1996.
5. A. Blake, P. Kohli, and C. Rother. Markov Random Fields for Vision and Image Processing. MIT Press, 2011.
6. T. Brox, A. Bruhn, N. Papenberg, and J. Weickert. High accuracy optical flow estimation based on a theory for warping. In ECCV, 2004.
7. T. Brox and J. Malik. Large displacement optical flow: Descriptor matching in variational motion estimation. PAMI, 33(3), 2011.
8. D. J. Butler, J. Wulff, G. B. Stanley, and M. J. Black. A naturalistic open source movie for optical flow evaluation. In ECCV, 2012.
9. Q. Chen and V. Koltun. Fast MRF optimization with application to depth reconstruction. In CVPR, 2014.
10. Q. Chen and V. Koltun. Robust nonrigid registration by convex optimization. In ICCV, 2015.
11. Z. Chen, H. Jin, Z. Lin, S. Cohen, and Y.Wu. Large displacement optical flow from nearest neighbor fields. In CVPR, 2013.
12. J. Choi and R. A. Rutenbar. FPGA acceleration of Markov random field TRW-S inference for stereo matching. In MEMCODE, 2013.
13. D. Eppstein. Sequence comparison with mixed convex and concave costs. J. Algorithms, 11(1), 1990.
14. P. F. Felzenszwalb and D. P. Huttenlocher. Efficient belief propagation for early vision. IJCV, 70(1), 2006.
15. P. F. Felzenszwalb and D. P. Huttenlocher. Distance transforms of sampled functions. Theory of Computing, 8(1), 2012.
16. A. Geiger, P. Lenz, C. Stiller, and R. Urtasun. Vision meets robotics: The KITTI dataset. International Journal of Robotics Research, 32(11), 2013.
17. B. Glocker, T. H. Heibel, N. Navab, P. Kohli, and C. Rother. TriangleFlow: Optical flow with triangulation-based higherorder likelihoods. In ECCV, 2010.
18. B. Glocker, N. Paragios, N. Komodakis, G. Tziritas, and N. Navab. Optical flow estimation with uncertainties through dynamic MRFs. In CVPR, 2008.
19. B. Goldluecke, E. Strekalovskiy, and D. Cremers. Tight convex relaxations for vector-valued labeling. SIAM J. Imaging Sciences, 6(3), 2013.
20. T. Goldstein, X. Bresson, and S. Osher. Global minimization of Markov random fields with applications to optical flow. Inverse Problems and Imaging, 6(4), 2012.
21. F. Heitz and P. Bouthemy. Multimodal estimation of discontinuous optical flow using Markov random fields. PAMI, 15(12), 1993.
22. B. K. P. Horn and B. G. Schunck. Determining optical flow. Artificial Intelligence, 17(1-3), 1981.
23. R. Kennedy and C. J. Taylor. Optical flow with geometric occlusion estimation and fusion of multiple frames. In EMMCVPR, 2015.
24. V. Kolmogorov. Convergent tree-reweighted message passing for energy minimization. PAMI, 28(10), 2006.
25. N. Komodakis, N. Paragios, and G. Tziritas. MRF energy minimization and beyond via dual decomposition. PAMI, 33(3), 2011.
26. J. Konrad and E. Dubois. Multigrid Bayesian estimation of image motion using stochastic relaxation. In ICCV, 1988.
27. K. J. Lee, D. Kwon, I. D. Yun, and S. U. Lee. Optical flow estimation with adaptive convolution kernel prior on discrete framework. In CVPR, 2010.
28. V. S. Lempitsky, C. Rother, S. Roth, and A. Blake. Fusion moves for Markov random field optimization. PAMI, 32(8), 2010.
29. M. Menze and A. Geiger. Object scene flow for autonomous vehicles. In CVPR, 2015.
30. M. Menze, C. Heipke, and A. Geiger. Discrete optimization for optical flow. In GCPR, 2015.
31. A. S. Ogale, C. Fermüller, and Y. Aloimonos. Motion segmentation using occlusions. PAMI, 27(6), 2005.
32. J. Revaud, P. Weinzaepfel, Z. Harchaoui, and C. Schmid. EpicFlow: Edge-preserving interpolation of correspondences for optical flow. In CVPR, 2015.
33. L. Sevilla-Lara, D. Sun, V. Jampani, and M. J. Black. Optical flow with semantic segmentation and localized layers. In CVPR, 2016.
34. A. Shekhovtsov, I. Kovtun, and V. Hlavác. Efficient MRF deformation model for non-rigid image matching. Computer Vision and Image Understanding, 112(1), 2008.
35. A. Shekhovtsov, C. Reinbacher, G. Graber, and T. Pock. Solving dense image matching in real-time using discretecontinuous optimization. In Proc. Computer Vision Winter Workshop, 2016.
36. M. V. Srinivasan. Honeybees as a model for the study of visually guided flight, navigation, and biologically inspired robotics. Physiological Reviews, 91(2), 2011.
37. F. Steinbrücker, T. Pock, and D. Cremers. Large displacement optical flow computation without warping. In ICCV, 2009.
38. E. Strekalovskiy, A. Chambolle, and D. Cremers. Convex relaxation of vectorial problems with coupled regularization. SIAM J. Imaging Sciences, 7(1), 2014.
39. D. Sun, S. Roth, and M. J. Black. A quantitative analysis of current practices in optical flow estimation and the principles behind them. IJCV, 106(2), 2014.
40. R. Szeliski, R. Zabih, D. Scharstein, O. Veksler, V. Kolmogorov, A. Agarwala, M. F. Tappen, and C. Rother. A comparative study of energy minimization methods for Markov random fields with smoothness-based priors. PAMI, 30(6), 2008.
41. C. Vogel, S. Roth, and K. Schindler. An evaluation of data costs for optical flow. In GCPR, 2013.
42. M. J. Wainwright, T. Jaakkola, and A. S. Willsky. MAP estimation via agreement on trees: message-passing and linear programming. IEEE Transactions on Information Theory, 51(11), 2005.
43. W. H. Warren. Self-motion: Visual perception and visual control. In Perception of Space and Motion. Academic Press, 1995.
44. P. Weinzaepfel, J. Revaud, Z. Harchaoui, and C. Schmid. DeepFlow: Large displacement optical flow with deep matching. In ICCV, 2013.
45. L. Xu, J. Jia, and Y. Matsushita. Motion detail preserving optical flow estimation. PAMI, 34(9), 2012.
46. J. Yang and H. Li. Dense, accurate optical flow estimation with piecewise parametric model. In CVPR, 2015.
47. C. Zach. A principled approach for coarse-to-fine MAP inference. In CVPR, 2014.