all resolutions & 5.66% & 13.01% & 1.27 px & 2.98 px\
nolowpass &[5.21%]{} & [12.21%]{} & [1.19 px]{} & [2.80 px]{}\
ms res 2+ & 5.18% & 12.12% & 1.21 px & 2.84 px\

Evaluation {#eval}
==========

We examine our approach on the KITTI 2012 training set [@geiger2013vision] as it is one of the few datasets that contains ground truth for non-synthetic large displacement optical flow estimation. We use patches taken from 130 of the 194 images of the set for training and patches from the remaining 64 images for validation. Each tested network is trained with 10 million negative and 10 million positive samples in total. Furthermore, we publicly validate the performance of our approach by submitting our results to the KITTI 2012, the recently published KITTI 2015 [@menze2015object] and MPI-Sintel evaluation portals (with networks trained on the respective training set). We use the original parameters of the *Flow Fields* approach [@bailer2015flow] except for the outlier filter distance $\epsilon$ and the random search distance $R$. $\epsilon$ is set to the best value for each network (with accuracy $\pm 0.25$, mostly: $\epsilon = 1.5$). The random search distance $R$ is set to 2 for four iterations and to $R=1$ 