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This work addresses the problem of semantic scene understanding under densefog. Although considerable progress has been made in semantic sceneunderstanding, it is mainly related to clear-weather scenes. Extendingrecognition methods to adverse weather conditions such as fog is crucial foroutdoor applications. In this paper, we propose a novel method, namedCurriculum Model Adaptation (CMAda), which gradually adapts a semanticsegmentation model from light synthetic fog to dense real fog in multiplesteps, using both synthetic and real foggy data. In addition, we present threeother main stand-alone contributions: 1) a novel method to add synthetic fog toreal, clear-weather scenes using semantic input; 2) a new fog densityestimator; 3) the Foggy Zurich dataset comprising $3808$ real foggy images,with pixel-level semantic annotations for $16$ images with dense fog. Ourexperiments show that 1) our fog simulation slightly outperforms astate-of-the-art competing simulation with respect to the task of semanticfoggy scene understanding (SFSU); 2) CMAda improves the performance ofstate-of-the-art models for SFSU significantly by leveraging unlabeled realfoggy data. The datasets and code are publicly available.

Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding