present a pipeline to solve a problem like optical flow estimation or 3D reconstruction, although many of them use 3D reconstruction datasets for evaluation. Zagoruyko and Komodakis [@zagoruyko2015learning] compared different architectures to compare patches. Simo-Serra et al. [@simo2015discriminative] used the Siamese architecture [@bromley1993signature] with $L_2$ distance. They argued that it is the most useful one for practical applications.

Recently, several successful CNN based approaches for stereo matching appeared [@zbontar2016stereo; @luo2016efficient; @mayer2015large]. However, so far there are still few approaches that successfully use learning to compute optical flow. Worth mentioning is FlowNet [@fischer2015flownet]. They tried to solve the optical flow problem as a whole with CNNs, having the images as CNN input and the optical flow as output. While the results are good regarding runtime, they are still not state-of-the-art quality. Also, the network is tailored for a specific image resolution and to our knowledge training for large images of several megapixel is still beyond todays computational capacity.

A first approach using patch matching with CNN based features is PatchBatch [@gadot2015patchbatch]. They managed to obtain state-of-the-art results on the KITTI dataset [@geiger2013vision], due to pixel-wise batch normalization and a loss that includes batch statistics. However, pixel-wise batch normalization is computationally expensive 