This work addresses the problem of semantic scene understanding under dense fog. Although considerable progress has been made in semantic scene understanding, it is mainly related to clear-weather scenes. Extending recognition methods to adverse weather conditions such as fog is crucial for outdoor applications. In this paper, we propose a novel method, named Curriculum Model Adaptation (CMAda), which gradually adapts a semantic segmentation model from light synthetic fog to dense real fog in multiple steps, using both synthetic and real foggy data. In addition, we present three other main stand-alone contributions: 1) a novel method to add synthetic fog to real, clear-weather scenes using semantic input; 2) a new fog density estimator; 3) the Foggy Zurich dataset comprising $3808$ real foggy images, with pixel-level semantic annotations for $16$ images with dense fog. Our experiments show that 1) our fog simulation slightly outperforms a state-of-the-art competing simulation with respect to the task of semantic foggy scene understanding (SFSU); 2) CMAda improves the performance of state-of-the-art models for SFSU significantly by leveraging unlabeled real foggy data. The datasets and code are publicly available.

本文提出了一种新方法，名为Curriculum Model Adaptation（CMAda），该方法逐步从轻度合成雾到多步中的密集真实雾中，使用合成和真实雾数据将语义分割模型适应于真实的雾天情况。四个主要贡献：1）一种通过语义输入向真实的晴天场景添加合成雾的新方法；2）一种新的雾密度估计器；3）Foggy Zurich数据集，由3808个真实雾天图像组成16个具有密集雾级别的像素级语义注释。我们的实验表明，我们的雾模拟在语义雾气场景理解（SFSU）任务方面略优于最先进的相对竞争模拟；CMAda通过利用未标记的真实雾天数据显著提高了SFSU的最先进模型的性能。数据集和代码公开。

基于合成和真实数据的模型适应性用于语义密集雾天场景理解