of it have an important design flaw: they try to decrease the $L_2$ distance unlimitedly for correct matches, although very small distances for patches that differ due to effects like illumination changes or partial occlusion are not only very costly but also unnecessary, as long as false matches have larger $L_2$ distances. We demonstrate that we can significantly increase the matching quality by relaxing this flaw.

Furthermore, we present a novel way to calculate CNN based features for the scales of Flow Fields [@bailer2015flow], which clearly outperforms the original multi-scale feature creation approach, with respect to CNN based features. Doing so, an important finding is that low-pass filtering CNN based feature maps robustly improves the matching quality.

Moreover, we introduce a novel matching robustness measure that is tailored for binary decision problems like patch matching (while ROC and PR are tailored for classification problems). By plotting the measure over different displacements and distances between a wrong patch and the correct one we can reveal interesting properties of different loss functions and scales. Our main contributions are:

1.  A novel loss function, that clearly outperforms other state-of-the art losses in our tests and allows to speed up training by a factor 