  Adverse weather conditions can severely affect the performance of LiDAR
sensors by introducing unwanted noise in the measurements. Therefore,
differentiating between noise and valid points is crucial for the reliable use
of these sensors. Current approaches for detecting adverse weather points
require large amounts of labeled data, which can be difficult and expensive to
obtain. This paper proposes a label-efficient approach to segment LiDAR point
clouds in adverse weather. We develop a framework that uses few-shot semantic
segmentation to learn to segment adverse weather points from only a few labeled
examples. Then, we use a semi-supervised learning approach to generate
pseudo-labels for unlabelled point clouds, significantly increasing the amount
of training data without requiring any additional labeling. We also integrate
good weather data in our training pipeline, allowing for high performance in
both good and adverse weather conditions. Results on real and synthetic
datasets show that our method performs well in detecting snow, fog, and spray.
Furthermore, we achieve competitive performance against fully supervised
methods while using only a fraction of labeled data.
