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Recently, convolutional networks (convnets) have proven useful for predicting optical flow.
Determining optical flow
B.K.P. Horn and B.G. Schunck · 1981
Earlier work this paper cites.
Large displacement optical flow: Descriptor matching in variational motion estimation
T. Brox and J. Malik · 2011
Earlier work this paper cites.
Vision meets robotics: The KITTI dataset
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun · 2013
Earlier work this paper cites.
DeepFlow: Large displacement optical flow with deep matching
P. Weinzaepfel, J. Revaud, Z. Harchaoui, and C. Schmid · 2013
Earlier work this paper cites.
A quantitative analysis of current practices in optical flow estimation and the principles behind them
D.Q. Sun, S. Roth, and M.J. Black · 2014
Earlier work this paper cites.
FlowNet: Learning optical flow with convolutional networks
A. Dosovitskiy, P. Fischer, 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.
Deeply-supervised nets
C. Lee, S. Xie, P. Gallagher, Z. Zhang, and Z. Tu · 2015
Cited alongside, same era.
Spatio-temporal video autoencoder with differentiable memory
V. Patraucean, A. Handa, and R. Cipolla · 2015
Cited alongside, same era.
EpicFlow: Edge-preserving interpolation of correspondences for optical flow
J. Revaud, P. Weinzaepfel, Z. Harchaoui, and C. Schmid · 2015
Cited alongside, same era.
lmb.informatik.uni-freiburg.de/resources/software.php
FlowNet Caffe code (v1.0)
Cited in the paper.
Virtual worlds as proxy for multi-object tracking analysis
A. Gaidon, Q. Wang, Y. Cabon, and E. Vig · 2016
Closest in time.
Unsupervised CNN for single view depth estimation: Geometry to the rescue
R. Garg, V. Kumar BG, and I.D. Reid · 2016
Closest in time.
A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
N. Mayer, E. Ilg, P. Hausser, P. Fischer, D. Cremers, A. Dosovitskiy, and T. Brox · 2016
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Deep end2end voxel2voxel prediction
D. Tran, L.D. Bourdev, R. Fergus, L. Torresani, and M. Paluri · 2016
Closest in time.
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