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We present structured domain randomization (SDR), a variant of domainrandomization (DR) that takes into account the structure and context of thescene. In contrast to DR, which places objects and distractors randomlyaccording to a uniform probability distribution, SDR places objects anddistractors randomly according to probability distributions that arise from thespecific problem at hand. In this manner, SDR-generated imagery enables theneural network to take the context around an object into consideration duringdetection. We demonstrate the power of SDR for the problem of 2D bounding boxcar detection, achieving competitive results on real data after training onlyon synthetic data. On the KITTI easy, moderate, and hard tasks, we show thatSDR outperforms other approaches to generating synthetic data (VKITTI, Sim200k, or DR), as well as real data collected in a different domain (BDD100K).Moreover, synthetic SDR data combined with real KITTI data outperforms realKITTI data alone.

Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data