https://github.com/henzler/platonicgan
Escaping Plato’s Cave: 3D Shape from Adversarial Rendering [ICCV 2019]
https://github.com/henzler/platonicgan
3d-reconstruction artificial-intelligence computer-graphics computer-vision deeplearning differentiable-rendering iccv2019 machine-learning
Last synced: 6 months ago
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Escaping Plato’s Cave: 3D Shape from Adversarial Rendering [ICCV 2019]
- Host: GitHub
- URL: https://github.com/henzler/platonicgan
- Owner: henzler
- Created: 2019-12-13T15:37:26.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2021-06-10T09:24:48.000Z (over 4 years ago)
- Last Synced: 2025-03-28T00:41:38.673Z (7 months ago)
- Topics: 3d-reconstruction, artificial-intelligence, computer-graphics, computer-vision, deeplearning, differentiable-rendering, iccv2019, machine-learning
- Language: Python
- Homepage: https://geometry.cs.ucl.ac.uk/projects/2019/platonicgan/
- Size: 22.6 MB
- Stars: 44
- Watchers: 3
- Forks: 11
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
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README
# PlatonicGAN
This repository contains code for the paper [Escaping Plato’s Cave: 3D Shape from Adversarial Rendering](https://geometry.cs.ucl.ac.uk/projects/2019/platonicgan/paper_docs/platonicgan.pdf) (ICCV2019).
More detailed information and results can be found on our [project page](https://geometry.cs.ucl.ac.uk/projects/2019/platonicgan/).
## Data
For training you can use any image collection you would like. For now two example datasets "tree" and "chanterelle" are provided under 'datasets/.
If you have your own data set go to 'scripts/data' and have a look at the already implemented custom implementations for data sets.
You may want to adjust them accordingly or create new ones.
In case you want to create a new `Dataset` class you should do so under `scripts/data`.
## Usage
### Prerequisites
- Linux (not tested for MacOS or Windows)
- Python3
- CPU or NVIDIA GPU
### Installation
Clone this repo:
```
git clone https://github.com/henzler/platonicgan
cd platonicgan
```
In order to install the required python packages run (in a new virtual environment):
```
pip install -r requirements.txt
```
*Note:* The model was trained with PyTorch 1.0.0, but also tested with Pytorch 1.3.1
### Train
For training execute the following command (for default config, currently this will train the tree dataset):
`python train.py`
or for a specific config:
`python train.py --config_file=scripts/configs/your_config_file.yaml`
### Track training progress
Use tensorboard to visualise intermediate training results:
`tensorboard --logdir=.`
## Bibtex
If you use this code for your research, please cite our paper.
```
@InProceedings{henzler2019platonicgan,
author = {Henzler, Philipp and Mitra, Niloy J. and Ritschel, Tobias},
title = {Escaping Plato's Cave: 3D Shape From Adversarial Rendering},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}
```