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

Abstract

We present Kaolin, a PyTorch library aiming to accelerate 3D deep learning research. Kaolin provides efficient implementations of differentiable 3D modules for use in deep learning systems. With functionality to load and preprocess several popular 3D datasets, and native functions to manipulate meshes, pointclouds, signed distance functions, and voxel grids, Kaolin mitigates the need to write wasteful boilerplate code. Kaolin packages together several differentiable graphics modules including rendering, lighting, shading, and view warping. Kaolin also supports an array of loss functions and evaluation metrics for seamless evaluation and provides visualization functionality to render the 3D results. Importantly, we curate a comprehensive model zoo comprising many state-of-the-art 3D deep learning architectures, to serve as a starting point for future research endeavours. Kaolin is available as open-source software at https://github.com/NVIDIAGameWorks/kaolin/.

Kaolin is available as open-source software at https://github.com/NVIDIAGameWorks/kaolin/

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• Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
• Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning
• GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects

Cited by in corpus (12)

• Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications
• Differentiable Rendering: A Survey
• Accelerating 3D Deep Learning with PyTorch3D
• Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesis
• AvatarMe++: Facial Shape and BRDF Inference with Photorealistic Rendering-Aware GANs
• Learning Deformable Tetrahedral Meshes for 3D Reconstruction
• gradSim: Differentiable simulation for system identification and visuomotor control
• DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer
• RobustPointSet: A Dataset for Benchmarking Robustness of Point Cloud Classifiers
• Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects
• Pose2RGBD. Generating Depth and RGB images from absolute positions
• 3DStyleNet: Creating 3D Shapes with Geometric and Texture Style Variations