Semantic Understanding of Foggy Scenes
with Purely Synthetic Data

Home Publications Semantic Understanding of Foggy Scenes with Purely Synthetic Data


Abstract

This work addresses the problem of semantic scene understanding under foggy road conditions.

Although marked progress has been made in semantic scene understanding over the recent years, it is mainly concentrated on clear weather outdoor scenes. Extending semantic segmentation methods to adverse weather conditions like fog is crucially important for outdoor applications such as self-driving cars.

In our paper, we propose a novel method, which uses purely synthetic data to improve the performance on unseen real-world foggy scenes captured in the streets of Zurich and its surroundings. Our results highlight the potential and power of photo-realistic synthetic images for training and especially fine-tuning deep neural nets.

Pipeline

Publication

Semantic Understanding of Foggy Scenes with Purely Synthetic Data
Martin Hahner, Dengxin Dai, Christos Sakaridis, Jan-Nico Zaech and Luc Van Gool
IEEE Intelligent Transportation Systems Conference (ITSC) 2019
[Paper]   [Code]   [BibTeX]   [arXiv]   [ICVSS 2019 Poster]   [ITSC 2019 Slides]

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 = {IEEE International Conference on Intelligent Transportation Systems (ITSC)},
  year = {2019},
}

@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},
}

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