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/.
翻译:我们介绍Kaolin,这是一家旨在加速3D深层学习研究的PyTorrch图书馆。Kaolin为深层学习系统提供高效的、可互换的3D模块应用。通过装载和预处理几个流行的3D数据集的功能和本地功能来操纵Mishes、点球、签名的距离功能和 voxel 电网,Kaolin 减轻了写浪费的锅炉板代码的需要。Kaolin 软件包将若干不同的图形模块结合在一起,包括制成、照明、阴影和视图。Kaolin 也支持一系列无缝评估的损失功能和评价指标,并提供可视化功能,使3D结果产生。 重要的是,我们设计了一个由许多最先进的3D深层学习结构组成的综合模型,作为未来研究工作的起点。Kaolin作为开放源软件可在 https://github.com/NVIDIAGameWorks/kaolin/上查阅。