to calculate the feature maps for the third and fourth scale applied at 50% and 25% resolution, respectively. For the multi-resolution CNN, the probability to select a patch on a lower resolution for training is set to be $60\%$ of the probability for the respective next higher resolution. For lower resolutions, we also use the distribution $N(p^+_2)$. This leads to a more wide spread distribution with respect to the full image resolution.

Feature maps created by our CNNs are not used directly. Instead, we perform a 2x low-pass filter on them, before using them. Low-pass filtering image data creates matching invariance while increasing ambiguity (by removing high frequency information). Assuming that CNNs are unable to create perfect matching invariance, we can expect a similar effect on feature maps created by CNNs. In fact, a small low-pass filter clearly increases the matching robustness.

The *Flow Fields* approach [@bailer2015flow] uses a secondary consistency check with different patch size. With our approach, this would require to train and execute two additional CNNs. To keep it simple, we perform the secondary check with the same features. This is possible due to the fact that *Flow Fields* is a randomized approach. Still, our tests with 