The Omniverse Kaolin Training Visualizer extension allows interactive visualization of 3D checkpoints exported using Kaolin Library python API. By scrubbing through iterations, one can see the progression of training over time, and visualize multiple textures and labels that may be predicted for each 3D model. The 3D checkpoints can include meshes, point clouds and voxel grids in any number of categories, with multiple textures and labels supported for meshes. The extension also allows creating and saving custom layouts for visualizing results consistently across experiments.
The performance of machine learning models can depend heavily on the properties of the training data. The Omniverse Kaolin Dataset Visualizer extension allows sampling and visualizing batches from 3D datasets to gain intuition and identify problems that can hinder learning.
Many machine learning techniques rely on images and ground truth labels for training, and synthetic data is a powerful tool to support such applications. The Omniverse Kaolin Dataset Renderer extension uses RTX ray and path tracing to render massive image datasets from a collection of 3D data, while also exporting custom ground truth labels from a variety of sensors.