FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
| Autor: | Ilg, Eddy, Mayer, Nikolaus, Saikia, Tonmoy, Keuper, Margret, Dosovitskiy, Alexey, Brox, Thomas |
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| Rok vydání: | 2016 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: Including supplementary material. For the video see: http://lmb.informatik.uni-freiburg.de/Publications/2016/IMKDB16/ |
| Databáze: | arXiv |
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