Kaolin: A Pytorch Library for Accelerating 3D Deep Learning Research

Overview

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.

Kaolin library is part of a larger suite of tools for 3D deep learning research. For example, the Omniverse Kaolin App allows interactive visualization of 3D checkpoints. To find out more about the Kaolin ecosystem, visit the NVIDIA Kaolin Dev Zone page.

Installation and Getting Started

Visit the Kaolin Library Documentation to get started!

About the Latest Release (0.10.0)

With the version 0.10.0 we are focusing on Volumetric rendering, adding new features for tetrahedral meshes, including DefTet volumetric renderer and losses, and Deep Marching Tetrahedrons, and adding new primitive operations for efficient volumetric rendering of Structured Point Clouds, we are also adding support for materials with USD importation.

Finally we are adding two new tutorials to show how to use the latest features from Kaolin. See Tutorial Index for more.

• How to use DMtet to rencontruct a mesh from point clouds generated by the Omniverse Kaolin App

• An Introduction to Structured Point Clouds, with conversion from mesh and interactive visualization with raytracing.

See change logs for details.

Contributing

Please review our contribution guidelines.

External Projects using Kaolin

• gradSim: Differentiable simulation for system identification and visuomotor control:
  • Use DIB-R rasterizer, obj loader and timelapse
• Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer:
  • Use Kaolin's DIB-R rasterizer, camera functions and Timelapse for 3D checkpoints.
• Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Surfaces:
  • Use SPC conversions and ray-tracing, yielding 30x memory and 3x training time reduction.
• Learning Deformable Tetrahedral Meshes for 3D Reconstruction:
  • Use Kaolin's DefTet volumetric renderer, tetrahedral losses, camera_functions, mesh operators and conversions, ShapeNet dataset, point_to_mesh_distanceand sided_distance.
• Text2Mesh:
  • Use Kaolin's rendering functions, camera functions, and obj and off importers.

Citation

If you are using Kaolin library for your research, please cite:

@misc{KaolinLibrary,
      author = {Fuji Tsang, Clement and Shugrina, Maria and Lafleche, Jean Francois and Takikawa, Towaki and Wang, Jiehan and Loop, Charles and Chen, Wenzheng and Jatavallabhula, Krishna Murthy and Smith, Edward and Rozantsev, Artem and Perel, Or and Shen, Tianchang and Gao, Jun and Fidler, Sanja and State, Gavriel and Gorski, Jason and Xiang, Tommy and Li, Jianing and Li, Michael and Lebaredian, Rev},
      title = {Kaolin: A Pytorch Library for Accelerating 3D Deep Learning Research},
      year = {2022},
      howpublished={\url{https://github.com/NVIDIAGameWorks/kaolin}}
}

Contributors

Current Team:

• Technical Lead: Clement Fuji Tsang
• Manager: Maria (Masha) Shugrina
• Jean-Francois Lafleche
• Charles Loop
• Or Perel
• Towaki Takikawa
• Jiehan Wang

Other Majors Contributors:

• Wenzheng Chen
• Sanja Fidler
• Jun Gao
• Jason Gorski
• Rev Lebaredian
• Jianing Li
• Michael Li
• Krishna Murthy Jatavallabhula
• Artem Rozantsev
• Tianchang (Frank) Shen
• Edward Smith
• Gavriel State
• Tommy Xiang