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FlowNet2, the state-of-the-art convolutional neural network (CNN) for optical flow estimation, requires over 160M parameters to achieve accurate flow estimation.
Determining optical flow
B. K. P. Horn and B. G. Schunck · 1981
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Learning parameterized models of image motion
M. J. Black, Y. Yacoobt, A. D. Jepsont, and D. J. Fleets · 1997
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High accuracy optical flow estimation based on a theory for warping
T. Brox, A. Bruhn, N. Papenberg, and J. Weickert · 2004
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On the spatial statistics of optical flow
S. Roth and M. Black · 2005
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Fields of experts: A framework for learning image priors
S. Roth and M. J. Black · 2005
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Vector-valued image regularization with PDEs: A common framework for different applications
D. Tschumperlé and R. Deriche · 2005
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Highly accurate optic flow computation with theoretically justified warping
N. Papenberg, A. Bruhn, T. Brox, S. Didas, and J. Weickert · 2006
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Over-parameterized variational optical flow
T. Nir, A. M. Bruckstein, and R. Kimmel · 2008
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Learning optical flow
D. Sun, S. Roth, J. Lewis, and M. J. Black · 2008
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PatchMatch: A randomized correspondence algorithm for structural image editing
C. Barnes, E. Shechtman, A. Finkelstein, and D. B. Goldman · 2009
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Anisotropic Huber-L
M. Werlberger, W. Trobin, T. Pock, A. Wedel, D. Cremers, and H. Bischof · 2009
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A database and evaluation methodology for optical flow
S. Baker, D. Scharstein, J. Lewis, S. Roth, M. J. Black, and R. Szeliski · 2011
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Large displacement optical flow: Descriptor matching in variational motion estimation
T. Brox and J. Mailk · 2011
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Optic flow in harmony
H. Zimmer, A. Bruhn, and J. Weickert · 2011
Cited alongside, same era.
A naturalistic open source movie for optical flow evaluation
D. J. Butler, J. Wulff, G. B. Stanley, and M. J. Black · 2012
Cited alongside, same era.
Are we ready for autonomous driving?
A. Geiger, P. Lenz, and R. Urtasun · 2012
Cited alongside, same era.
Determining motion directly from normal flows upon the use of a spherical eye platform
T.-W. Hui and R. Chung · 2013
Cited alongside, same era.
PatchMatch Filter: Efficient edge-aware filtering meets randomized search
J. Lu, H. Yang, D. Min, and M. N. Do · 2013
Cited alongside, same era.
Learning the local statistics of optical flow
D. Rosenbaum, D. Zoran, and Y. Weiss · 2013
Cited alongside, same era.
Object scene flow for autonomous vehicles
M. Menze and A. Geiger · 2015
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EpicFlow: Edge-preserving interpolation of correspondences for optical flow
J. Revaud, P. Weinzaepfel, Z. Harchaoui, and C. Schmid · 2015
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U-Net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
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Efficient sparse-to-dense optical flow estimation using a learned basis and layers
J. Wulff and M. J. Black · 2015
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Learning to compare image patches via convolutional neural networks
S. Zagoruyko and N. Komodakis · 2015
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Deep discrete flow
F. Güney and A. Geiger · 2016
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DeepFlow: Large displacement optical flow with deep matching
P. Weinzaepfel, J. Revaud, Z. Harchaoui, and C. Schmid · 2013
Cited alongside, same era.
A quantitative analysis of current practices in optical flow estimation and the principles behind them
D. Sun, S. Roth, and M. J. Black · 2014
Cited alongside, same era.
DeepFace: Closing the gap to human-level performance in face verification
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf · 2014
Cited alongside, same era.
Flow Fields: Dense correspondence fields for highly accurate large displacement optical flow estimation
C. Bailer, B. Taetz, and D. Stricker · 2015
Cited alongside, same era.
FlowNet: Learning optical flow with convolutional networks
P. Fischer, A. Dosovitskiy, E. Ilg, P. Häusser, C. Hazirbas, V. Golkov, P. van der Smagt, D. Cremers, and T. Brox · 2015
Cited alongside, same era.
Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu · 2015
Cited alongside, same era.
A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
N. Mayer, E. Ilg, P. Häusser, P. Fischer, D. Cremers, A. Dosovitskiy, and T. Brox · 2016
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CNN-based patch matching for optical flow with thresholded hinge embedding loss
C. Bailer, K. Varanasi, and D. Stricker · 2017
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FlowNet2.0: Evolution of optical flow estimation with deep networks
E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy, and T. Brox · 2017
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Optical flow estimation using a spatial pyramid network
A. Ranjan and M. J. Black · 2017
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Accurate optical flow via direct cost volume processings
J. Xu, R. Ranftl, and V. Koltun · 2017
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InterpoNet, A brain inspired neural network for optical flow dense interpolation
S. Zweig and L. Wolf · 2017
Later among the works it cites.