FlowNet 2.0: Evolution of Optical Flow Estimation with Deep NetworksFlowNet 2.0: Evolution of Optical Flow Estimation with Deep NetworksEddy Ilg and Nikolaus Mayer and Tonmoy Saikia and Margret Keuper and Alexey Dosovitskiy and Thomas Brox2016
Paper summaryeddiesmolansky- Implementations:
- https://hub.docker.com/r/mklinov/caffe-flownet2/
- https://github.com/lmb-freiburg/flownet2-docker
- https://github.com/lmb-freiburg/flownet2
- Explanations:
- A Brief Review of FlowNet - not a clear explanation
https://medium.com/towards-data-science/a-brief-review-of-flownet-dca6bd574de0
- https://www.youtube.com/watch?v=JSzUdVBmQP4
Supplementary material:
http://openaccess.thecvf.com/content_cvpr_2017/supplemental/Ilg_FlowNet_2.0_Evolution_2017_CVPR_supplemental.pdf
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
Eddy Ilg
and
Nikolaus Mayer
and
Tonmoy Saikia
and
Margret Keuper
and
Alexey Dosovitskiy
and
Thomas Brox
arXiv e-Print archive - 2016 via Local arXiv
Keywords:
cs.CV
First published: 2016/12/06 (4 years ago) Abstract: The FlowNet demonstrated that optical flow estimation can be cast as a
learning problem. However, the state of the art with regard to the quality of
the flow has still been defined by traditional methods. Particularly on small
displacements and real-world data, FlowNet cannot compete with variational
methods. In this paper, we advance the concept of end-to-end learning of
optical flow and make it work really well. The large improvements in quality
and speed are caused by three major contributions: first, we focus on the
training data and show that the schedule of presenting data during training is
very important. Second, we develop a stacked architecture that includes warping
of the second image with intermediate optical flow. Third, we elaborate on
small displacements by introducing a sub-network specializing on small motions.
FlowNet 2.0 is only marginally slower than the original FlowNet but decreases
the estimation error by more than 50%. It performs on par with state-of-the-art
methods, while running at interactive frame rates. Moreover, we present faster
variants that allow optical flow computation at up to 140fps with accuracy
matching the original FlowNet.