  With the increasing availability of large databases of 3D CAD models,
depth-based recognition methods can be trained on an uncountable number of
synthetically rendered images. However, discrepancies with the real data
acquired from various depth sensors still noticeably impede progress. Previous
works adopted unsupervised approaches to generate more realistic depth data,
but they all require real scans for training, even if unlabeled. This still
represents a strong requirement, especially when considering
real-life/industrial settings where real training images are hard or impossible
to acquire, but texture-less 3D models are available. We thus propose a novel
approach leveraging only CAD models to bridge the realism gap. Purely trained
on synthetic data, playing against an extensive augmentation pipeline in an
unsupervised manner, our generative adversarial network learns to effectively
segment depth images and recover the clean synthetic-looking depth information
even from partial occlusions. As our solution is not only fully decoupled from
the real domains but also from the task-specific analytics, the pre-processed
scans can be handed to any kind and number of recognition methods also trained
on synthetic data. Through various experiments, we demonstrate how this
simplifies their training and consistently enhances their performance, with
results on par with the same methods trained on real data, and better than
usual approaches doing the reverse mapping.
