  As synthetic imagery is used more frequently in training deep models, it is
important to understand how different synthesis techniques impact the
performance of such models. In this work, we perform a thorough evaluation of
the effectiveness of several different synthesis techniques and their impact on
the complexity of classifier domain adaptation to the "real" underlying data
distribution that they seek to replicate. In addition, we propose a novel
learned synthesis technique to better train classifier models than
state-of-the-art offline graphical methods, while using significantly less
computational resources. We accomplish this by learning a generative model to
perform shading of synthetic geometry conditioned on a "g-buffer"
representation of the scene to render, as well as a low sample Monte Carlo
rendered image. The major contributions are (i) a dataset that allows
comparison of real and synthetic versions of the same scene, (ii) an augmented
data representation that boosts the stability of learning and improves the
datasets accuracy, (iii) three different partially differentiable rendering
techniques where lighting, denoising and shading are learned, and (iv) we
improve a state of the art generative adversarial network (GAN) approach by
using an ensemble of trained models to generate datasets that approach the
performance of training on real data and surpass the performance of the full
global illumination rendering.
