LiDAR sensors provide high-resolution 3D perception and long-range detection, making them indispensable for autonomous driving and robotics. However, their performance significantly degrades under adverse weather conditions such as snow, rain, and fog, where spurious noise points dominate the point cloud and lead to false perception. To address this problem, various approaches have been proposed: distance-based filters exploiting spatial sparsity, intensity-based filters leveraging reflectance distributions, and learning-based methods that adapt to complex environments. Nevertheless, distance-based methods struggle to distinguish valid object points from noise, intensity-based methods often rely on fixed thresholds that lack adaptability to changing conditions, and learning-based methods suffer from the high cost of annotation, limited generalization, and computational overhead. In this study, we propose LIORNet, which eliminates these drawbacks and integrates the strengths of all three paradigms. LIORNet is built upon a U-Net++ backbone and employs a self-supervised learning strategy guided by pseudo-labels generated from multiple physical and statistical cues, including range-dependent intensity thresholds, snow reflectivity, point sparsity, and sensing range constraints. This design enables LIORNet to distinguish noise points from environmental structures without requiring manual annotations, thereby overcoming the difficulty of snow labeling and the limitations of single-principle approaches. Extensive experiments on the WADS and CADC datasets demonstrate that LIORNet outperforms state-of-the-art filtering algorithms in both accuracy and runtime while preserving critical environmental features. These results highlight LIORNet as a practical and robust solution for LiDAR perception in extreme weather, with strong potential for real-time deployment in autonomous driving systems.

翻译：激光雷达传感器能够提供高分辨率的三维感知与远距离探测能力，使其成为自动驾驶与机器人技术中不可或缺的核心部件。然而，在雪、雨、雾等恶劣天气条件下，其性能会显著下降——点云中充斥的噪声点会导致感知出现偏差。为解决该问题，研究者提出了多种方法：基于空间稀疏性的距离滤波、利用反射率分布信息的强度滤波，以及能够适应复杂环境的基于学习的方法。然而，距离类方法难以区分有效目标点与噪声，强度类方法常依赖固定阈值而缺乏对动态环境的适应性，基于学习的方法则面临标注成本高、泛化能力有限及计算开销大等挑战。本研究提出LIORNet框架，该框架消除了上述缺陷，并融合了三种范式的优势。LIORNet以U-Net++为主干网络，采用基于多物理与统计线索生成伪标签的自监督学习策略，这些线索包括距离相关强度阈值、雪反射特性、点稀疏度及感知距离约束。该设计使LIORNet无需人工标注即可区分噪声点与环境结构，从而克服了雪点标注难题及单一原理方法的局限性。在WADS与CADC数据集上的大量实验表明，LIORNet在精度与运行效率上均优于现有最优滤波算法，同时保留关键环境特征。这些结果证实LIORNet是极端天气下激光雷达感知的实用稳健方案，在自动驾驶系统的实时部署方面具有显著潜力。