  Performance on benchmark datasets has drastically improved with advances in
deep learning. Still, cross-dataset generalization performance remains
relatively low due to the domain shift that can occur between two different
datasets. This domain shift is especially exaggerated between synthetic and
real datasets. Significant research has been done to reduce this gap,
specifically via modeling variation in the spatial layout of a scene, such as
occlusions, and scene environmental factors, such as time of day and weather
effects. However, few works have addressed modeling the variation in the sensor
domain as a means of reducing the synthetic to real domain gap. The camera or
sensor used to capture a dataset introduces artifacts into the image data that
are unique to the sensor model, suggesting that sensor effects may also
contribute to domain shift. To address this, we propose a learned augmentation
network composed of physically-based augmentation functions. Our proposed
augmentation pipeline transfers specific effects of the sensor model --
chromatic aberration, blur, exposure, noise, and color temperature -- from a
real dataset to a synthetic dataset. We provide experiments that demonstrate
that augmenting synthetic training datasets with the proposed learned
augmentation framework reduces the domain gap between synthetic and real
domains for object detection in urban driving scenes.
