  We present structured domain randomization (SDR), a variant of domain
randomization (DR) that takes into account the structure and context of the
scene. In contrast to DR, which places objects and distractors randomly
according to a uniform probability distribution, SDR places objects and
distractors randomly according to probability distributions that arise from the
specific problem at hand. In this manner, SDR-generated imagery enables the
neural network to take the context around an object into consideration during
detection. We demonstrate the power of SDR for the problem of 2D bounding box
car detection, achieving competitive results on real data after training only
on synthetic data. On the KITTI easy, moderate, and hard tasks, we show that
SDR outperforms other approaches to generating synthetic data (VKITTI, Sim
200k, or DR), as well as real data collected in a different domain (BDD100K).
Moreover, synthetic SDR data combined with real KITTI data outperforms real
KITTI data alone.
