  Recent developments established deep learning as an inevitable tool to boost
the performance of dense matching and stereo estimation. On the downside,
learning these networks requires a substantial amount of training data to be
successful. Consequently, the application of these models outside of the
laboratory is far from straight forward. In this work we propose a
self-supervised training procedure that allows us to adapt our network to the
specific (imaging) characteristics of the dataset at hand, without the
requirement of external ground truth data. We instead generate interim training
data by running our intermediate network on the whole dataset, followed by
conservative outlier filtering. Bootstrapped from a pre-trained version of our
hybrid CNN-CRF model, we alternate the generation of training data and network
training. With this simple concept we are able to lift the completeness and
accuracy of the pre-trained version significantly. We also show that our final
model compares favorably to other popular stereo estimation algorithms on an
aerial dataset.
