Unity Perception: Generate Synthetic Data for Computer Vision
This provides a practical tool for researchers and developers in computer vision to efficiently create synthetic data, though it is incremental as it builds on existing simulation and data generation methods.
The authors tackled the challenge of generating synthetic datasets for computer vision by introducing the Unity Perception package, which simplifies and accelerates this process with an easy-to-use, customizable toolset, and demonstrated its value by training a 2D object detection model that outperformed one trained only on real data.
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.