  Realistic synthetic image data rendered from 3D models can be used to augment
image sets and train image classification semantic segmentation models. In this
work, we explore how high quality physically-based rendering and domain
randomization can efficiently create a large synthetic dataset based on
production 3D CAD models of a real vehicle. We use this dataset to quantify the
effectiveness of synthetic augmentation using U-net and Double-U-net models. We
found that, for this domain, synthetic images were an effective technique for
augmenting limited sets of real training data. We observed that models trained
on purely synthetic images had a very low mean prediction IoU on real
validation images. We also observed that adding even very small amounts of real
images to a synthetic dataset greatly improved accuracy, and that models
trained on datasets augmented with synthetic images were more accurate than
those trained on real images alone. Finally, we found that in use cases that
benefit from incremental training or model specialization, pretraining a base
model on synthetic images provided a sizeable reduction in the training cost of
transfer learning, allowing up to 90\% of the model training to be
front-loaded.
