Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research   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/.

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The categories of this paper are: math
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The categories of this paper are: supr-con
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The categories of this paper are: atom-ph
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The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
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The categories of this paper are: comp-gas
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The categories of this paper are: math-ph
16
The categories of this paper are: stat
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The categories of this paper are: q-bio
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The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-th
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The categories of this paper are: hep-lat