超越雪花：在激光雷达目标检测中解耦天气效应与混淆元因素
Beyond the Snowflakes: Disentangling Weather Effects from Confounding Meta-Factors in LiDAR Object Detection
AI总结：
该研究针对LiDAR目标检测中雪天性能的反直觉现象，通过WADS-3D和CADC数据集分析，发现雪天目标点数量增加抵消了降雪噪声的负面影响，提出需解耦大气噪声与表面积累效应以准确评估检测器鲁棒性。
摘要：
激光雷达（Light Detection and Ranging, LiDAR）对自主感知至关重要，但其性能在恶劣天气下会下降。现有文献将雪建模为降低性能的大气噪声，但我们对WADS-3D和CADC数据集的细粒度评估揭示了一个反直觉现象：检测准确率随雪强度增加而持续提升。通过消融研究和元信息分析，我们调和了这一矛盾：尽管降雪是噪声，但其负面影响被一个主导的并发因素——显著增加的目标点数量所抵消。我们假设，这种目标点数量的提升是一种几何增强，由积雪增加车辆表面积以及从镜面反射向漫反射的转变驱动。我们的发现表明，使用宽泛的天气强度区间评估感知系统，而不考虑相关的目标级数据，会导致关于检测器鲁棒性的误导性结论。我们得出结论，准确的评估需要将大气噪声与表面积累效应解耦，并概述了验证这些物理模型的未来研究。WADS-3D数据集可在以下网址获取：this https URL。
英文摘要：
Light Detection and Ranging (LiDAR) is critical for autonomous perception, yet its performance is known to degrade in adverse weather. While existing literature models snow as atmospheric noise that degrades performance, our granular evaluation on the WADS-3D and CADC datasets reveals a counter-intuitive phenomenon: detection accuracy consistently improves as snow intensity increases. Through ablation study and meta-information analysis, we reconcile this by confirming that while falling snow acts as noise, its negative impact is outweighed by a dominant, concurrent factor: significantly increased object point count. We hypothesize that this object point count boost acts as a geometric enhancement, driven by snow accumulation increasing vehicle surface area and a shift from specular to diffuse reflectance. Our findings demonstrate that evaluating perception systems using broad weather-severity bins without accounting for correlated object-level data can lead to misleading conclusions about detector robustness. We conclude that accurate evaluation requires decoupling atmospheric noise from surface accumulation effects and outline future research to validate these physical models. The WADS-3D dataset is available at: https://digitalcommons.mtu.edu/wads-3d/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
