Photo-Realistic Simulation of Road Scene for Data-Driven Methods in Bad Weather

Modern data-driven computer vision algorithms require a large volume, varied data for validation or evaluation. We utilize computer graphics techniques to generate a large volume foggy image dataset of road scenes with different levels of fog. We compare with other popular synthesized datasets, including data collected both from the virtual world and the real world. In addition, we benchmark recent popular dehazing methods and evaluate their performance on different datasets, which provides us an objectively comparison of their limitations and strengths. To our knowledge, this is the first foggy and hazy dataset with large volume data which can be helpful for computer vision research in the autonomous driving.

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