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Signed-off-by: Kosuke Takeuchi <kosuke.tnp@gmail.com> Signed-off-by: Shivam Tawari <shivamtawari@gmail.com>
Jean-Francois-Lafleche
approved these changes
Aug 18, 2020
wow6600
reviewed
Sep 3, 2020
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| **[Documentation](https://kaolin.readthedocs.io/en/latest/)** | **[Paper](https://arxiv.org/abs/1911.05063)** | |||
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| __Kaolin is an active work-in-progress, we are working toward a stable beta release. Please Watch our github repository for releases to be notified. To start trying our code today see instructions below.__ | |||
| __Kaolin is an active work-in-progress, we are working toward a stable beta release. Please Watch our GitHub repository for releases to be notified. To start trying our code today see instructions below.__ | |||
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| Kaolin is 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. | |||
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gh pr checkout 303 |
kyrie25
approved these changes
Feb 5, 2022
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Grammatical mistakes corrected.