  The usefulness of deep learning models in robotics is largely dependent on
the availability of training data. Manual annotation of training data is often
infeasible. Synthetic data is a viable alternative, but suffers from domain
gap. We propose a multi-step method to obtain training data without manual
annotation effort: From 3D object meshes, we generate images using a modern
synthesis pipeline. We utilize a state-of-the-art image-to-image translation
method to adapt the synthetic images to the real domain, minimizing the domain
gap in a learned manner. The translation network is trained from unpaired
images, i.e. just requires an un-annotated collection of real images. The
generated and refined images can then be used to train deep learning models for
a particular task. We also propose and evaluate extensions to the translation
method that further increase performance, such as patch-based training, which
shortens training time and increases global consistency. We evaluate our method
and demonstrate its effectiveness on two robotic datasets. We finally give
insight into the learned refinement operations.
