  We present a system for training deep neural networks for object detection
using synthetic images. To handle the variability in real-world data, the
system relies upon the technique of domain randomization, in which the
parameters of the simulator$-$such as lighting, pose, object textures,
etc.$-$are randomized in non-realistic ways to force the neural network to
learn the essential features of the object of interest. We explore the
importance of these parameters, showing that it is possible to produce a
network with compelling performance using only non-artistically-generated
synthetic data. With additional fine-tuning on real data, the network yields
better performance than using real data alone. This result opens up the
possibility of using inexpensive synthetic data for training neural networks
while avoiding the need to collect large amounts of hand-annotated real-world
data or to generate high-fidelity synthetic worlds$-$both of which remain
bottlenecks for many applications. The approach is evaluated on bounding box
detection of cars on the KITTI dataset.
