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+# Adversarial Autoencoders for Compact Representations of 3D Point Clouds
+
+Authors: Maciej Zamorski, Maciej Zięba, Piotr Klukowski, Rafał Nowak, Karol Kurach, Wojciech Stokowiec, and Tomasz Trzciński
+
+![mainimg](https://github.com/MaciejZamorski/3d-AAE/blob/master/BinaryRepresentations.jpg)
+
+## Introduction
+This is a PyTorch implementation for a family of 3dAAE models, a novel framework for learning continuous and binary representations of 3d point clouds based on Adversarial Autoencoder model, as presented in:
+
+M. Zamorski, M. Zięba, et al., Adversarial Autoencoders for Compact Representations of 3D Point Clouds, [arXiv preprint](https://arxiv.org/abs/1811.07605) (2018)
+## Citation
+```
+@article{zamorski2018adversarial,
+  title={Adversarial Autoencoders for Compact Representations of 3D Point Clouds},
+  author={Zamorski, Maciej and Zi{\k{e}}ba, Maciej and Klukowski, Piotr and Nowak, Rafa{\l} and Kurach, Karol and Stokowiec, Wojciech and Trzci{\'n}ski, Tomasz},
+  journal={arXiv preprint arXiv:1811.07605},
+  year={2018}
+}
+```
+
+## Requirements
+Stored in `requirements.txt`, Python dependencies are:
+```
+h5py
+matplotlib
+numpy
+pandas
+git+https://github.com/szagoruyko/pyinn.git@master
+torch==0.4.1
+```
+
+## Usage
+### Training
+Run an experiment with:
+
+`python3.6 experiments/train.py --config settings.json`
+
+where
+
+`train.py` - one of the training scripts from the `experiments` directory
+
+`settings.json` - JSON file with training settings and hyperparameter values created as shown in example `settings/hyperparams.json`
+
+### Evaluation
+`python3.6 evaluation/find_best_epoch_on_validation.py --config settings.json`
+
+Calculates JSD distance between sampled point clouds and the validation set and presents the best epoch.
+
+`python3.6 evaluation/generate_data_for_metrics.py --config settings.json`
+
+Produce reconstructed and generated point clouds in a form of NumPy array to be used with validation methods from ["Learning Representations and Generative Models For 3D Point Clouds" repository](https://github.com/optas/latent_3d_points/blob/master/notebooks/compute_evaluation_metrics.ipynb)