NVIDIA released a PyTorch library ‘Kaolin’, which in few steps, moves 3D models into neural networks. Kaolin helps in easy implementation of 3D modules for use in deep learning models.
Kaolin is developed with advanced functionalities to load and preprocess multiple 3D datasets and functions. The advanced nature of Kaolin helps in preparing 3D models for deep learning from 300 lines of codes to five lines.
In terms of applications, Kaolin can help researches in virtual and augmented reality, robotics, medical imaging, etc.
First create a virtual environment. In this example, we show how to create a
conda virtual environment for installing kaolin.
$ conda create --name kaolin python=3.6 $ conda activate kaolin
Now, install the dependencies (
torch). Note that the setup file does not automatically install these dependencies.
conda install numpy
Install PyTorch, by following instructions from https://pytorch.org/
Now, you can install the library. From the root directory of this repo (i.e., the directory containing this
README file), run
$ python setup.py install
During installation, the packman package manager will download the nv-usd package to
~/packman-repo/ containing the necessary packages for reading and writing Universal Scene Description (USD) files.
>>> import kaolin as kal >>> print(kal.__version__)
Installation Details: https://github.com/NVIDIAGameWorks/kaolin