  Autonomous vehicles rely on LiDAR sensors to perceive the environment.
Adverse weather conditions like rain, snow, and fog negatively affect these
sensors, reducing their reliability by introducing unwanted noise in the
measurements. In this work, we tackle this problem by proposing a novel
approach for detecting adverse weather effects in LiDAR data. We reformulate
this problem as an outlier detection task and use an energy-based framework to
detect outliers in point clouds. More specifically, our method learns to
associate low energy scores with inlier points and high energy scores with
outliers allowing for robust detection of adverse weather effects. In extensive
experiments, we show that our method performs better in adverse weather
detection and has higher robustness to unseen weather effects than previous
state-of-the-art methods. Furthermore, we show how our method can be used to
perform simultaneous outlier detection and semantic segmentation. Finally, to
help expand the research field of LiDAR perception in adverse weather, we
release the SemanticSpray dataset, which contains labeled vehicle spray data in
highway-like scenarios. The dataset is available at
https://semantic-spray-dataset.github.io .
