Getting Clever with Kaolin: Researchers Accelerate 3D Deep Learning with New Tools

by Lauren Finkle

3D deep learning holds the potential to accelerate progress in everything from robotics to medical imaging. But until now, researchers haven’t had the right tools to easily manage and visualize different types of 3D data.

NVIDIA Kaolin is a collection of tools within the NVIDIA Omniverse simulation and collaboration platform that allows researchers to visualize and generate datasets, move between 3D tools and retain basic functions for other users.

NVIDIA AI Podcast host Noah Kravitz spoke with four NVIDIANs about their work on the platform, including Richard Kerris, industry general manager for Omniverse; Jean-Francois Lafleche, a deep learning engineer; Senior Research Scientist Masha Shugrina; and Research Scientist Clement Fuji Tsang.

Kaolin includes both a library, which contains a growing number of GPU-optimized operations, and an app within NVIDIA Omniverse for interactive 3D data visualizations. The long-term goal is to make both facets so robust that users could import a photo that generates a highly developed 3D model without spending time on recreating the scene within a 3D platform.

Key Points From This Episode:

  • 3D data doesn’t just take the form of meshes — it can also manifest as point clouds, implicit functions and voxels. Each type of data requires a different tool, which researchers will need exporters and renderers to work across. NVIDIA Kaolin unites these tools for more efficient transfer across data types.
  • NVIDIA Kaolin is an open-source project, available on GitHub. While NVIDIA experts will continue to expand and improve the library, contributions from the community will ensure that it’s a beneficial tool for everyone.


“We’re taking all these tools and tying them together with the Kaolin library so that … it becomes really easy to do something like visualizing your data.” — Jean-Francois Lafleche [6:49]

“[Kaolin] really allows you to debug and understand your models better and more quickly.” — Masha Shugrina [13:19]

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