Label-Efficient Semantic Segmentation of LiDAR Point Clouds in Adverse Weather Conditions
This addresses the challenge of expensive data labeling for autonomous vehicles and robotics in adverse weather, though it is incremental as it builds on existing few-shot and semi-supervised techniques.
The paper tackles the problem of segmenting LiDAR point clouds in adverse weather conditions by proposing a label-efficient approach that uses few-shot semantic segmentation and semi-supervised learning with pseudo-labels, achieving competitive performance against fully supervised methods while using only a fraction of labeled data.
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.