  We introduce the Unity Perception package which aims to simplify and
accelerate the process of generating synthetic datasets for computer vision
tasks by offering an easy-to-use and highly customizable toolset. This
open-source package extends the Unity Editor and engine components to generate
perfectly annotated examples for several common computer vision tasks.
Additionally, it offers an extensible Randomization framework that lets the
user quickly construct and configure randomized simulation parameters in order
to introduce variation into the generated datasets. We provide an overview of
the provided tools and how they work, and demonstrate the value of the
generated synthetic datasets by training a 2D object detection model. The model
trained with mostly synthetic data outperforms the model trained using only
real data.
