Created by Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, Leonidas J. Guibas.

This work is based on our arXiv tech report. We proposed a novel deep net architecture for auto-encoding point clouds. The learned representations was amenable to xxx.

If you find our work useful in your research, please consider citing:

@article{achlioptas2017latent_pc,
  title={Learning Representations And Generative Models For 3D Point Clouds},
  author={Achlioptas, Panos and Diamanti, Olga and Mitliagkas, Ioannis and Guibas, Leonidas J},
  journal={arXiv preprint arXiv:1707.02392},
  year={2017}
}

Main requirements:

• Python 2.7+ with Numpy, Scipy and Matplotlib
• Tensorflow
• TFLearn

Our code has been tested with Python 2.7, TensorFlow 1.3.0, TFLearn 0.3.2, CUDA 8.0 and cuDNN 6.0 on Ubuntu 14.04.

Download the source code from the git repository:

mkdir -p $HOME/work
cd $HOME/work
git clone https://github.com/HoME-Platform/home-platform.git

Note that the library must be in the PYTHONPATH environment variable for Python to be able to find it:

export PYTHONPATH=$HOME/work/home-platform:$PYTHONPATH 

This can also be added at the end of the configuration file $HOME/.bashrc

This project is licensed under the terms of the MIT license (see LICENSE.md for details).