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

Starting with v0.12.0, Kaolin supports installation with wheels:

# Replace TORCH_VERSION and CUDA_VERSION with your torch / cuda versions
pip install kaolin==0.12.0 -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-{TORCH_VERSION}_cu{CUDA_VERSION}.html

For example, to install kaolin 0.13.0 over torch 1.12.1 and cuda 11.3:

pip install kaolin==0.13.0 -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-1.12.1_cu113.html

We now support version 0.12.0 to 0.13.0

Visit the Kaolin Library Documentation to get started!

About the Latest Release (0.13.0)

With the version 0.13.0 we have added new lighting features, most notably spherical gaussian diffuse and specular reflectance, we also improved the spherical harmonics API and coefficients.

See tutorials below.

We also:

Reformated the data preprocessing with a new CachedDataset replacing ProcessedDataset
Fixed bug and improved speed on SPC raytracing, and added gradient on trilinear interpolation
Improved memory consumption on uniform_laplacian

Check out our new tutorials:

Recipe for fast mesh sampling preprocessing in examples/recipes/preprocess/fast_mesh_sampling.py
Recipe for SPC 3d convolution in examples/recipes/spc/spc_conv3d_example.py
Tutorial for diffuse lighting in examples/tutorial/diffuse_lighting.ipynb
Tutorial for spherical gaussian specular lighting examples/tutorial/sg_specular_lighting.ipynb

See change logs for details.

Contributing

Please review our contribution guidelines.

External Projects using Kaolin

NVIDIA Kaolin Wisp:
- Use Camera API, Structured Point Clouds and its rendering capabilities
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}}
}