Semantic Foggy Scene Understanding with Synthetic Data

This work addresses the problem of semantic foggy scene understanding (SFSU). Although extensive research has been performed on image dehazing and on semantic scene understanding with weather-clear images, little attention has been paid to SFSU. Due to the difficulty of collecting and annotating foggy images, we choose to generate synthetic fog on real images that depict weather-clear outdoor scenes, and then leverage these synthetic data for SFSU by employing state-of-the-art convolutional neural networks (CNN). In particular, a complete pipeline to generate synthetic fog on real, weather-clear images using incomplete depth information is developed. We apply our fog synthesis on the Cityscapes dataset and generate Foggy Cityscapes with 20550 images. SFSU is tackled in two fashions: 1) with typical supervised learning, and 2) with a novel semi-supervised learning, which combines 1) with an unsupervised supervision transfer from weather-clear images to their synthetic foggy counterparts. In addition, this work carefully studies the usefulness of image dehazing for SFSU. For evaluation, we present Foggy Driving, a dataset with 101 real-world images depicting foggy driving scenes, which come with ground truth annotations for semantic segmentation and object detection. Extensive experiments show that 1) supervised learning with our synthetic data significantly improves the performance of state-of-the-art CNN for SFSU on Foggy Driving; 2) our semi-supervised learning strategy further improves performance; and 3) image dehazing marginally benefits SFSU with our learning strategy. The datasets, models and code will be made publicly available to encourage further research in this direction.