  We present a novel domain adaptation framework that uses morphologic
segmentation to translate images from arbitrary input domains (real and
synthetic) into a uniform output domain. Our framework is based on an
established image-to-image translation pipeline that allows us to first
transform the input image into a generalized representation that encodes
morphology and semantics - the edge-plus-segmentation map (EPS) - which is then
transformed into an output domain. Images transformed into the output domain
are photo-realistic and free of artifacts that are commonly present across
different real (e.g. lens flare, motion blur, etc.) and synthetic (e.g.
unrealistic textures, simplified geometry, etc.) data sets. Our goal is to
establish a preprocessing step that unifies data from multiple sources into a
common representation that facilitates training downstream tasks in computer
vision. This way, neural networks for existing tasks can be trained on a larger
variety of training data, while they are also less affected by overfitting to
specific data sets. We showcase the effectiveness of our approach by
qualitatively and quantitatively evaluating our method on four data sets of
simulated and real data of urban scenes. Additional results can be found on the
project website available at http://jonathank.de/research/eps/ .
