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FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

Technical Report , arXiv:1612.01925, Dec 2016
Download the publication : FlowNet_2_0_Supplemental__arXiv.pdf [5.7Mo]   FlowNet_2_0__arXiv.pdf [12.9Mo]  

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.

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BibTex references

@TechReport{IMKDB16,
  author       = "E. Ilg and N. Mayer and T. Saikia and M. Keuper and A. Dosovitskiy and T. Brox",
  title        = "FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks",
  institution  = "arXiv:1612.01925",
  month        = "Dec",
  year         = "2016",
  url          = "http://lmb.informatik.uni-freiburg.de//Publications/2016/IMKDB16"
}

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