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README.md

Semantic Understanding of Foggy Scenes with Purely Synthetic Data

Forked from github.com/sakaridis/fog_simulation-SFSU_synthetic.
Adapted by Martin Hahner at the Computer Vision Lab of ETH Zurich.

Introduction

This is the source code we used in our ITSC 2019 paper titled
Semantic Understanding of Foggy Scenes with Purely Synthetic Data,
to create Foggy Synscapes, a foggy version of the original Synscapes dataset.

foggy_images

The pipeline computes transmittance maps,
which can be combined with the original clear-weather image
to obtain foggy versions of the clear-weather image.

Pipeline Overview

pipeline

Getting Started

This demo runs our fog simulation pipeline on an example clear-weather image from Synscapes and writes the results to ./output/demo/.

Requirements

  • MATLAB
    The code has been developed in MATLAB R2018b and tested in MATLAB 2019a, too.

How to run the demo

  1. Clone this repository
    git clone https://github.com/MartinHahner88/FoggySynscapes.git
    
  2. Go to MATLAB's Command Window and assign FOG_SIMULATION_ROOT to the path you have cloned this repository into
    FOG_SIMULATION_ROOT = '/path/to/FoggySynscapes';
    
  3. Change MATLAB's current folder
    cd(fullfile(FOG_SIMULATION_ROOT, 'source'));
    
  4. Run Demo.m
    Demo;
    

Generating Foggy Synscapes all by yourself

Due to the license agreement of the Synscapes dataset,
we unfortunatelly cannot directly distribute Foggy Synscapes.

But if you follow the instructions below, you can reproduce Foggy Synscapes all by yourself. *

* given you have access to the original Synscapes dataset

Requirements

  • Synscapes
    You need the original dataset as a starting point.

  • Python
    The code has been developed and tested using Python 3.7.4.

  • MATLAB
    The code has been developed in MATLAB R2018b and tested in MATLAB 2019a, too.

How to reproduce Foggy Synscapes:

  1. Request the original Synscapes dataset by sending a kind email to the authors.

  2. Then you need to convert the .exr depth files provided by the Synscapes dataset to .mat depth files that are compatible to our fog simulation pipeline. You can do this for example by using the provided exr_to_mat.py python script.

  3. Finally, you can modify FoggySynscapes.m according to your needs and generate your own version of
    Foggy Synscapes.

Note: In our paper we used beta values of [0.005, 0.01, 0.02, 0.03, 0.06].

Citations

If you use our work, please cite our publication and the Synscapes White Paper:

@inproceedings{FoggySynscapes,
  author    = {Hahner, Martin and Dai, Dengxin and Sakaridis, Christos and Zaech, Jan-Nico and Van Gool, Luc},
  title     = {Semantic Understanding of Foggy Scenes with Purely Synthetic Data},
  booktitle = {Proceedings of the 22nd IEEE International Conference on Intelligent Transportation Systems}, 
  series    = {IEEE ITSC 2019},
  year      = {2019},
  month     = {Oct}
}

@article{Synscapes,
    author    = {Magnus Wrenninge and Jonas Unger},
    title     = {Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing},
    url       = {http://arxiv.org/abs/1810.08705},
    year      = {2018},
    month     = {Oct}
}

License

Our fog simulation pipeline is made available for non-commercial use only.
For details, please refer to the license agreement.

Contact

Martin Hahner
martin.hahner[at]vision.ee.ethz.ch
https://www.trace.ethz.ch/foggy_synscapes

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