  Learning-based stereo matching and depth estimation networks currently excel
on public benchmarks with impressive results. However, state-of-the-art
networks often fail to generalize from synthetic imagery to more challenging
real data domains. This paper is an attempt to uncover hidden secrets of
achieving domain robustness and in particular, discovering the important
ingredients of generalization success of stereo matching networks by analyzing
the effect of synthetic image learning on real data performance. We provide
evidence that demonstrates that learning of features in the synthetic domain by
a stereo matching network is heavily influenced by two "shortcuts" presented in
the synthetic data: (1) identical local statistics (RGB colour features)
between matching pixels in the synthetic stereo images and (2) lack of realism
in synthetic textures on 3D objects simulated in game engines. We will show
that by removing such shortcuts, we can achieve domain robustness in the
state-of-the-art stereo matching frameworks and produce a remarkable
performance on multiple realistic datasets, despite the fact that the networks
were trained on synthetic data, only. Our experimental results point to the
fact that eliminating shortcuts from the synthetic data is key to achieve
domain-invariant generalization between synthetic and real data domains.
