  Learning based approaches have not yet achieved their full potential in
optical flow estimation, where their performance still trails heuristic
approaches. In this paper, we present a CNN based patch matching approach for
optical flow estimation. An important contribution of our approach is a novel
thresholded loss for Siamese networks. We demonstrate that our loss performs
clearly better than existing losses. It also allows to speed up training by a
factor of 2 in our tests. Furthermore, we present a novel way for calculating
CNN based features for different image scales, which performs better than
existing methods. We also discuss new ways of evaluating the robustness of
trained features for the application of patch matching for optical flow. An
interesting discovery in our paper is that low-pass filtering of feature maps
can increase the robustness of features created by CNNs. We proved the
competitive performance of our approach by submitting it to the KITTI 2012,
KITTI 2015 and MPI-Sintel evaluation portals where we obtained state-of-the-art
results on all three datasets.
