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Over four decades, the majority addresses the problem of optical flow estimation using variational methods.
B. K. P. Horn and B. G. Schunck, “Determining optical flow,” Arifical Intelligence , vol. 17, pp. 185–203, 1981
1981
Earlier work this paper cites.
B. D. Lucas and T. Kanade, “An iterative image registration technique with an application to stereo vision,” IJCAI , pp. 674–679, 1981
1981
Earlier work this paper cites.
M. J. Black, Y. Yacoobt, A. D. Jepsont, and D. J. Fleets, “Learning parameterized models of image motion,” CVPR , pp. 674–679, 1997
1997
Earlier work this paper cites.
T. Brox, A. Bruhn, N. Papenberg, and J. Weickert, “High accuracy optical flow estimation based on a theory for warping,” ECCV , pp. 25–36, 2004
2004
Earlier work this paper cites.
S. Roth and M. J. Black, “Fields of experts: A framework for learning image priors,” CVPR , pp. 860–867, 2005
2005
Earlier work this paper cites.
S. Roth and M. Black, “On the spatial statistics of optical flow,” ICCV , pp. 42–49, 2005
2005
Earlier work this paper cites.
D. Tschumperlé and R. Deriche, “Vector-valued image regularization with PDEs: A common framework for different applications,” TPAMI , vol. 27, no. 4, pp. 506–517, 2005
2005
Earlier work this paper cites.
N. Papenberg, A. Bruhn, T. Brox, S. Didas, and J. Weickert, “Highly accurate optic flow computation with theoretically justified warping,” IJCV , vol. 67, no. 2, pp. 141–158, 2006
2006
Earlier work this paper cites.
J. Xiao, H. Cheng, H. Sawhney, C. Rao, and M. Isnardi, “Bilateral filtering-based optical flow estimation with occlusion detection,” ECCV , pp. 211–224, 2006
2006
Earlier work this paper cites.
D. Sun, S. Roth, J. Lewis, and M. J. Black, “Learning optical flow,” ECCV , pp. 83–97, 2008
2008
Earlier work this paper cites.
T. Nir, A. M. Bruckstein, and R. Kimmel, “Over-parameterized variational optical flow,” IJCV , vol. 76, no. 2, pp. 205–216, 2008
2008
Earlier work this paper cites.
M. Werlberger, W. Trobin, T. Pock, A. Wedel, D. Cremers, and H. Bischof, “Anisotropic Huber-L 1
2009
Earlier work this paper cites.
C. Barnes, E. Shechtman, A. Finkelstein, and D. B. Goldman, “Patchmatch: A randomized correspondence algorithm for structural image editing,” SIGGRAGH , pp. 83–97, 2009
2009
Earlier work this paper cites.
T. Brox and J. Mailk, “Large displacement optical flow: Descriptor matching in variational motion estimation,” TPAMI , vol. 33, no. 3, pp. 500–513, 2011
2011
Earlier work this paper cites.
H. Zimmer, A. Bruhn, and J. Weickert, “Optic flow in harmony,” IJCV , vol. 93, no. 3, pp. 368–388, 2011
2011
Earlier work this paper cites.
L. Xu, J. Jia, and Y. Matsushita, “Motion detail preserving optical flow estimation,” TPAMI , vol. 34, no. 9, pp. 1744–1757, 2012
2012
Earlier work this paper cites.
A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving?” CVPR , pp. 3354–3361, 2012
2012
Earlier work this paper cites.
D. J. Butler, J. Wulff, G. B. Stanley, and M. J. Black, “A naturalistic open source movie for optical flow evaluation,” ECCV , pp. 611–625, 2012
2012
Cited alongside, same era.
T.-W. Hui and R. Chung, “Determining motion directly from normal flows upon the use of a spherical eye platform,” CVPR , pp. 2267–2274, 2013
2013
Cited alongside, same era.
——, “Determining shape and motion from non-overlapping multi-camera rig: A direct approach using normal flows,” CVIU , vol. 117, no. 8, pp. 947–964, 2013
2013
Cited alongside, same era.
T. H. Kim, H. S. Lee, and K. M. Lee, “Optical flow via locally adaptive fusion of complementary data costs,” ICCV , pp. 2373–2381, 2013
2013
Cited alongside, same era.
P. Weinzaepfel, J. Revaud, Z. Harchaoui, and C. Schmid, “DeepFlow: Large displacement optical flow with deep matching,” ICCV , pp. 500–513, 2013
2013
S. Zagoruyko and N. Komodakis, “Learning to compare image patches via convolutional neural networks,” CVPR , pp. 4353–4361, 2015
2015
Later among the works it cites.
C. Bailer, B. Taetz, and D. Stricker, “Flow Fields: Dense correspondence fields for highly accurate large displacement optical flow estimation,” ICCV , pp. 4015–4023, 2015
2015
Later among the works it cites.
B. D. Brabandere, X. Jia, T. Tuytelaars, and L. V. Gool, “Dynamic filter networks,” NIPS , 2016
2016
Later among the works it cites.
F. Güney and A. Geiger, “Deep discrete flow,” ACCV , 2016
2016
Later among the works it cites.
J. J. Yu, A. W. Harley, and K. G. Derpanis, “Back to Basics: Unsupervised learning of optical flow via brightness constancy and motion smoothness,” ECCVW , pp. 3–10, 2016
2016
Later among the works it cites.
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Cited alongside, same era.
J. Lu, H. Yang, D. Min, and M. N. Do, “PatchMatch Filter: Efficient edge-aware filtering meets randomized search,” CVPR , pp. 1854–1861, 2013
2013
Cited alongside, same era.
D. Rosenbaum, D. Zoran, and Y. Weiss, “Learning the local statistics of optical flow,” NIPS , pp. 2373–2381, 2013
2013
Cited alongside, same era.
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, “DeepFace: Closing the gap to human-level performance in face verification,” CVPR , pp. 1701–1708, 2014
2014
Cited alongside, same era.
D. Sun, S. Roth, and M. J. Black, “A quantitative analysis of current practices in optical flow estimation and the principles behind them,” IJCV , vol. 106, no. 2, pp. 115–137, 2014
2014
Cited alongside, same era.
——, “Determining shape and motion from monocular camera: A direct approach using normal flows,” PR , vol. 48, no. 2, pp. 422–437, 2015
2015
Cited alongside, same era.
A. Dosovitskiy, P. Fischer, E. Ilg, P. Häusser, C. Hazırbaş, V. Golkov, P. van der Smagt, D. Cremers, and T. Brox, “FlowNet: Learning optical flow with convolutional networks,” ICCV , pp. 2758–2766, 2015
2015
Cited alongside, same era.
M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu, “Spatial transformer networks,” NIPS , pp. 2017–2025, 2015
2015
Cited alongside, same era.
N. Mayer, E. Ilg, P. Häusser, P. Fischer, D. Cremers, A. Dosovitskiy, and T. Brox, “A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation,” CVPR , pp. 4040–4048, 2016
2016
Later among the works it cites.
D. Kondermann, R. Nair, K. Honauer, K. Krispin, J. Andrulis, A. Brock, B. Gussefeld, M. Rahimimoghaddam, and S. Hofmann, “The HCI benchmark suite: Stereo and flow ground truth with uncertainties for urban autonomous driving,” CVPR Workshops , pp. 19–28, 2016
2016
Later among the works it cites.
E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy, and T. Brox, “FlowNet2.0: Evolution of optical flow estimation with deep networks,” CVPR , pp. 2462–2470, 2017
2017
Later among the works it cites.
A. Ranjan and M. J. Black, “Optical flow estimation using a spatial pyramid network,” CVPR , pp. 4161–4170, 2017
2017
Later among the works it cites.
S. Zweig and L. Wolf, “Interponet, a brain inspired neural network for optical flow dense interpolation,” CVPR , pp. 6363–6372, 2017
2017
Later among the works it cites.
C. Bailer, K. Varanasi, and D. Stricker, “CNN-based patch matching for optical flow with thresholded hinge embedding loss,” CVPR , pp. 3250–3259, 2017
2017
Later among the works it cites.
J. Xu, R. Ranftl, and V. Koltun, “Accurate optical flow via direct cost volume processings,” CVPR , pp. 1289–1297, 2017
2017
Later among the works it cites.
2018
Later among the works it cites.
T.-W. Hui, X. Tang, and C. C. Loy, “LiteFlowNet: A lightweight convolutional neural network for optical flow estimation,” CVPR , pp. 8981–8989, 2018
2018
Later among the works it cites.
D. Sun, X. Yang, M.-Y. Liu, and J. Kautz, “PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume,” CVPR , pp. 8934–8943, 2018
2018
Later among the works it cites.
D. Sun, X. Yang, M.-Y. Liu, and J. Kautz, “Models matter, so does training: An empirical study of CNNs for optical flow estimation,” TPAMI , 2019
2019
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