https://github.com/aakarsh/latent_3d_points

Auto-encoding & Generating 3D Point-Clouds.

https://github.com/aakarsh/latent_3d_points

Repository

Auto-encoding & Generating 3D Point-Clouds.

https://github.com/aakarsh/latent_3d_points/blob/master/

# Learning Representations and Generative Models For 3D Point Clouds
Created by Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, Leonidas J. Guibas.

![representative](https://github.com/optas/latent_3d_points/blob/master/doc/images/teaser.jpg)


## Introduction
This work is based on our [arXiv tech report](https://arxiv.org/abs/1707.02392). We proposed a novel deep net architecture for auto-encoding point clouds. The learned representations were amenable to semantic part editting, shape analogies, linear classification and shape interpolations.



## 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
Requirements:
- Python 2.7+ with Numpy, Scipy and Matplotlib
- [Tensorflow (version 1.0+)](https://www.tensorflow.org/get_started/os_setup)
- [TFLearn](http://tflearn.org/installation)

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.


## Installation
Download the source code from the git repository:
```
git clone https://github.com/optas/latent_3d_points
```

To be able to train your own model you need first to _compile_ the EMD/Chamfer losses. In latent_3d_points/external/structural_losses we have included the cuda implementations of [Fan et. al](https://github.com/fanhqme/PointSetGeneration).
```
cd latent_3d_points/external

with your editor modify the first three lines of the makefile to point to 
your nvcc, cudalib and tensorflow library.

make
```

### Data Set
We provide ~57K point-clouds, each sampled from a mesh model of 
ShapeNetCore 
with (area) uniform sampling. To download them (1.4GB):
```
cd latent_3d_points/
./download_data.sh
```
The point-clouds will be stored in latent_3d_points/data/shape_net_core_uniform_samples_2048

Use the function snc_category_to_synth_id, defined in src/in_out/, to map a class name such as "chair" to its synthetic_id: "03001627". Point-clouds of models of the same class are stored under a commonly named folder.


### Usage
To train a point-cloud AE look at:

    latent_3d_points/notebooks/train_single_class_ae.ipynb

To train a latent-GAN based on a pre-trained AE look at:

    latent_3d_points/notebooks/train_latent_gan.ipynb

To train a raw-GAN:

    latent_3d_points/notebooks/train_raw_gan.ipynb    

To use the evaluation metrics (MMD, Coverage, JSD) between two point-cloud sets look at:

    latent_3d_points/notebooks/compute_evaluation_metrics.ipynb



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

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