  Deep learning methods typically require vast amounts of training data to
reach their full potential. While some publicly available datasets exists,
domain specific data always needs to be collected and manually labeled, an
expensive, time consuming and error prone process. Training with synthetic data
is therefore very lucrative, as dataset creation and labeling comes for free.
We propose a novel method for creating purely synthetic training data for
object detection. We leverage a large dataset of 3D background models and
densely render them using full domain randomization. This yields background
images with realistic shapes and texture on top of which we render the objects
of interest. During training, the data generation process follows a curriculum
strategy guaranteeing that all foreground models are presented to the network
equally under all possible poses and conditions with increasing complexity. As
a result, we entirely control the underlying statistics and we create optimal
training samples at every stage of training. Using a set of 64 retail objects,
we demonstrate that our simple approach enables the training of detectors that
outperform models trained with real data on a challenging evaluation dataset.
