AI/ML

Semantic Understanding of Foggy Scenes with Purely Synthetic Data. (arXiv:1910.03997v2 [cs.CV] UPDATED)




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 this 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. Our contributions are threefold, 1) we
created a purely synthetic, high-quality foggy dataset of 25,000 unique outdoor
scenes, that we call Foggy Synscapes and plan to release publicly 2) we show
that with this data we outperform previous approaches on real-world foggy test
data 3) we show that a combination of our data and previously used data can
even further improve the performance on real-world foggy data.

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