  The availability of real data from areas with high privacy requirements, such
as the medical intervention space, is low and the acquisition legally complex.
Therefore, this work presents a way to create a synthetic dataset for the
medical context, using medical clothing as an example. The goal is to close the
reality gap between the synthetic and real data. For this purpose, methods of
3D-scanned clothing and designed clothing are compared in a
Domain-Randomization and Structured-Domain-Randomization scenario using an
Unreal-Engine plugin or Unity. Additionally a Mixed-Reality dataset in front of
a greenscreen and a target domain dataset were used. Our experiments show, that
Structured-Domain-Randomization of designed clothing together with
Mixed-Reality data provide a baseline achieving 72.0% mAP on a test dataset of
the clinical target domain. When additionally using 15% of available target
domain train data, the gap towards 100% (660 images) target domain train data
could be nearly closed 80.05% mAP (81.95% mAP). Finally we show that when
additionally using 100% target domain train data the accuracy could be
increased to 83.35% mAP.
