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.

该论文提出了一种标签效率的方法，通过使用少量已标记示例来学习从非常少量标记的点云中分割恶劣天气的点，并利用半监督学习方法生成伪标签，从而显著增加训练数据量，同时还在训练过程中整合了好的天气数据，从而在良好和恶劣天气条件下均能取得高性能。在真实和合成数据集上的结果显示，我们的方法在检测雪、雾和水雾方面表现良好。此外，我们在使用的标记数据只有一小部分的情况下，实现了与完全监督方法相竞争的性能。