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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}
}

Installation

Install TensorFlow and 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.

License

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

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Auto-encoding & Generating 3D Point-Clouds.

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