Boosting Perception Model Training with Synthetic Data

Nyla Worker is a Product Manager at NVIDIA. She talks here about her company’s Omniverse Replicator, which is a synthetic data generation tool that allows users to create or augment datasets and use them to train perception models. Because many ML projects stall due to a lack of good real-world data, using synthetic data enables users to add data and then troubleshoot and test their models before the development process gets too far underway. Omniverse provides users with realistic synthetic data, and it is a platform that is useful across a variety of industries, including engineering, media and game development, robotics, and autonomous vehicles.
Replicator is a framework built on top of an Omniverse platform that enables physically accurate 3D synthetic data generation without the need for a lengthy preliminary data-collection process. It accelerates the training and performance of AI perception networks, allowing teams to test ideas quickly, using synthetic data, in order to determine if the idea warrants further investment. This has particular potential in 3D learning, for instance in the development of autonomous vehicles.
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