  One of the grand challenges of deep learning is the requirement to obtain
large labeled training data sets. While synthesized data sets can be used to
overcome this challenge, it is important that these data sets close the reality
gap, i.e., a model trained on synthetic image data is able to generalize to
real images. Whereas, the reality gap can be considered bridged in several
application scenarios, training on synthesized images containing reflecting
materials requires further research. Since the appearance of objects with
reflecting materials is dominated by the surrounding environment, this
interaction needs to be considered during training data generation. Therefore,
within this paper we examine the effect of reflecting materials in the context
of synthetic image generation for training object detectors. We investigate the
influence of rendering approach used for image synthesis, the effect of domain
randomization, as well as the amount of used training data. To be able to
compare our results to the state-of-the-art, we focus on indoor scenes as they
have been investigated extensively. Within this scenario, bathroom furniture is
a natural choice for objects with reflecting materials, for which we report our
findings on real and synthetic testing data.
