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How to parse the 3D deep learning Pytorch library Kaolin, many novices are not very clear about this, in order to help you solve this problem, the following small series will explain in detail for everyone, there are people who need this to learn, I hope you can gain something.

The authors propose Kaolin, a PyTorch library designed to accelerate 3D deep learning research. The name Kaolin comes from kaolinite, which means kaolinite in Chinese. It is also known as "kaolin" and "porcelain clay," a clay mineral sometimes used in 3D modeling.

Kaolin implements a variety of modules that can be used for 3D deep learning. It has the ability to load and preprocess commonly used 3D datasets, including ShapeNet, PartNet, SHREC, ModelNet, ScanNet and HumanSeg. It is also very convenient to call up. Take ModelNet as an example. The code is:

The Kaolin library can be used to process 3D data of all kinds, such as meshes, point clouds, signed distance functions, and voxel grids, thus relieving researchers of the need to repeatedly write code to build wheels. Kaolin packages several graphics modules together, including rendering, lighting, shadows, and view morphing, which are very useful. In addition, Kaolin supports a range of loss functions and evaluation metrics for seamless evaluation and provides visualization capabilities to render 3D results.

Importantly, the authors have also built a comprehensive library of network models, including many of the most advanced 3D deep learning architectures, that can be used in research. You can see that this includes some familiar network architectures, such as PointNet++. It is also very convenient to use.

Moreover, the network version implemented in this library runs faster than the original version, such as the following networks:

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