SkylerHuang / GraphCNN-GAN-codeonly

Graph-convolutional GAN for point cloud generation. Code from ICLR 2019 paper Learning Localized Generative Models for 3D Point Clouds via Graph Convolution. Code only.

Learning Localized Generative Models for 3D Point Clouds via Graph Convolution (ICLR 2019)

Code-only repository. Full repository with trained models (large files!): https://github.com/diegovalsesia/GraphCNN-GAN

If you like our work, please cite the journal version of the paper.

Journal version BibTex reference:

@ARTICLE{Valsesia2019journal,
author={Diego {Valsesia} and Giulia {Fracastoro} and Enrico {Magli}},
journal={under review},
title={Learning Localized Representations of Point Clouds with Graph-Convolutional Generative Adversarial Networks},
year={2019},
volume={},
number={},
pages={},
}

ICLR 2019 BibTex reference:

@inproceedings{valsesia2019learning,
  title={Learning Localized Generative Models for 3D Point Clouds via Graph Convolution},
  author={Valsesia, Diego and Fracastoro, Giulia and Magli, Enrico},
  booktitle={International Conference on Learning Representations (ICLR) 2019},
  year={2019}
}

Requirements

• Python 2.7
• Tensorflow >=1.6

Usage

A trained model for the method with aggregation upsampling is provided for the following Shapenet classes: airplane, chair, sofa, table.

• launch_test.sh : generate a batch of point clouds from the specified class
• launch_train.sh : retrain the network (requires downloading the Shapenet dataset and place it in the data directory)