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This work addresses the challenging task of LiDAR-based 3D object detectionin foggy weather. Collecting and annotating data in such a scenario is verytime, labor and cost intensive. In this paper, we tackle this problem bysimulating physically accurate fog into clear-weather scenes, so that theabundant existing real datasets captured in clear weather can be repurposed forour task. Our contributions are twofold: 1) We develop a physically valid fogsimulation method that is applicable to any LiDAR dataset. This unleashes theacquisition of large-scale foggy training data at no extra cost. Thesepartially synthetic data can be used to improve the robustness of severalperception methods, such as 3D object detection and tracking or simultaneouslocalization and mapping, on real foggy data. 2) Through extensive experimentswith several state-of-the-art detection approaches, we show that our fogsimulation can be leveraged to significantly improve the performance for 3Dobject detection in the presence of fog. Thus, we are the first to providestrong 3D object detection baselines on the Seeing Through Fog dataset. Ourcode is available at www.trace.ethz.ch/lidar_fog_simulation.

Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather