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.
@article{arxiv.2107.04259,
title = {Unity Perception: Generate Synthetic Data for Computer Vision},
author = {Steve Borkman and Adam Crespi and Saurav Dhakad and Sujoy Ganguly and Jonathan Hogins and You-Cyuan Jhang and Mohsen Kamalzadeh and Bowen Li and Steven Leal and Pete Parisi and Cesar Romero and Wesley Smith and Alex Thaman and Samuel Warren and Nupur Yadav},
journal= {arXiv preprint arXiv:2107.04259},
year = {2021}
}
Comments
We corrected tasks supported by NVISII platform. For the Unity perception package, see https://github.com/Unity-Technologies/com.unity.perception