Synthetic Data

Training AI models or running large-scale 3D simulations requires carefully labeled and diverse datasets that contain thousands to tens of millions of elements, some of which are beyond the RGB spectrum. Collecting and labeling this data in the real world is time-consuming and prohibitively expensive. This can hinder the development of AI models and slow down the time to solution. 

Synthetic data, generated by algorithms and containing annotated information such as images or text, can be used in conjunction with real-world data to train AI models and create Universal Scene Description (OpenUSD)-based 3D simulations. Synthetic data generation (SDG) can save significant time and greatly reduce costs.