Kaolin is a suite of tools for accelerating 3D Deep Learning research.
NVIDIA Kaolin library provides a PyTorch API for working with a variety of 3D representations and includes a growing collection of GPU-optimized operations such as modular differentiable rendering, fast conversions between representations, data loading, 3D checkpoints and more.
NVIDIA Omniverse Kaolin App is an interactive application for 3D deep learning researchers that allows inspecting 3D datasets, interactive visualization of 3D outputs of a model during training, and synthetic dataset rendering. Built on Omniverse Kit, the research application benefits from high-fidelity RTX rendering and will gain new functionality periodically from new extensions.
In combination, these tools can massively reduce the time needed to develop AI research for a wide range of 3D applications.
Modular Differentiable Renderer
Develop cutting-edge inverse graphics applications using modular and optimized mesh differentiable renderer.
3D Data Loading
Easily load large 3D datasets to train your machine learning models. Make use of import and export utilities for OBJ and USD formats.
GPU Optimized 3D Operations
Convert between 3D representations using fast and reliable conversion operations. Use GPU-optimized implementations of 3D loss functions and a growing collection of other operations on 3D data.
Export 3D checkpoints for meshes, point clouds and voxel grids in USD format, allowing interactive visualization of model training.
Omniverse Kaolin App
Leveraging the NVIDIA Omniverse Platform, Omniverse Kaolin App allows high fidelity rendering and interactive visualization of 3D data and training results.
Visualize Model Training
Scrub through iterations of 3D checkpoints exported using the Kaolin python API to understand how your model is training. Debug models in development by inspecting 3D outputs and produce beautiful renderings of final results.
Render Synthetic Data
Render massive training datasets with RTX ray and path tracing and export useful ground truth labels, such as segmentation maps, bounding boxes, depth maps and normals.
Inspect 3D Datasets
Effortlessly sample, render and inspect 3D datasets to gain intuition about your training data and identify inconsistencies.