  This work addresses the challenging task of LiDAR-based 3D object detection
in foggy weather. Collecting and annotating data in such a scenario is very
time, labor and cost intensive. In this paper, we tackle this problem by
simulating physically accurate fog into clear-weather scenes, so that the
abundant existing real datasets captured in clear weather can be repurposed for
our task. Our contributions are twofold: 1) We develop a physically valid fog
simulation method that is applicable to any LiDAR dataset. This unleashes the
acquisition of large-scale foggy training data at no extra cost. These
partially synthetic data can be used to improve the robustness of several
perception methods, such as 3D object detection and tracking or simultaneous
localization and mapping, on real foggy data. 2) Through extensive experiments
with several state-of-the-art detection approaches, we show that our fog
simulation can be leveraged to significantly improve the performance for 3D
object detection in the presence of fog. Thus, we are the first to provide
strong 3D object detection baselines on the Seeing Through Fog dataset. Our
code is available at www.trace.ethz.ch/lidar_fog_simulation.
