Isaac Sim: Generating synthetic data using Replicator Composer

Training perceptual models requires large and diverse datasets. Assembling these data sets can be time-consuming, expensive, dangerous, and in some extreme cases impossible. Using Isaac Sim's Omniverse Replicator, developers can launch training tasks. In the early stages of a project, synthetic data can speed up proof-of-concept or validation ML workflows. In later stages of the development cycle, synthetic data can be used to augment real data, reducing the time it takes to train product models. Isaac Sim has built-in support for domain randomization, allowing changes to textures, colors, lighting, and placement. It also supports different types of data, including bounding boxes, depth, and segmentations. Developers can more easily leverage NVIDIA's TAO tool suite by exporting datasets in KITTI format.

Isaac Sim: Generating synthetic data using Replicator Composer

[Introduction to synthetic data generation tutorial (2): Synthetic data production tool—Omniverse CODE/Replicator installation

Official introduction

How to train a defect detection model using synthetic data generated by NVIDIA Omniverse Replicator

In fact, in the real world, it is not always possible to obtain sufficient ground-truth images. Developers can use
synthetic data generated by NVIDIA Omniverse Replicator to close the gap between simulation and reality