We present structured domain randomization (SDR), a variant of domain randomization (DR) that takes into account the structure of the scene in order to add context to the generated data. 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
REF uses structured domain randomization, which allows the network to take context into account.
Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data
2019 International Conference on Robotics and Automation (ICRA)
2019 International Conference on Robotics and Automation (ICRA)
Computer Science Engineering