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Learning Representations And Generative Models For 3D Point Clouds

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

Introduction

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.

Citation

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}
}

Dependencies

Main requirements:

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.

Installing the library

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

License

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