of around two.

2.  A novel multi-scale feature creation approach tailored for CNN features for optical flow.

3.  New evaluation measure of matching robustness for optical flow and corresponding plots.

4.  We show that low-pass filtering the feature maps created by CNNs improves matching robustness.

5.  We demonstrate the effectiveness of our approach by obtaining a top performance on all three major evaluation portals KITTI 2012 [@geiger2013vision], 2015 [@menze2015object] and MPI-Sintel [@butler2012naturalistic]. Former learning based approaches always trailed heuristic approaches on at least one of them.

Related Work
============

While regularized optical flow estimation goes back to Horn and Schunck [@horn1981determining], randomized patch matching [@barnes2009patchmatch] is a relatively new field, first successfully applied in approximate nearest neighbor estimation where the data term is well-defined. The success in optical flow estimation (where the data term is not well-defined) started with publications like [@bao2014fast; @chen2013large]. One of the most recent works is Flow Fields [@bailer2015flow], which showed that with proper multi-scale patch matching, top performing optical flow results can be achieved.

Regarding patch or descriptor matching with learned data terms, there exists a fair amount of literature [@han2015matchnet; @simo2015discriminative; @zagoruyko2015learning; @simonyan2014learning]. These approaches treat matching at an abstract level and do not 