提出分束统计背景剔除法，提升路边激光雷达动态目标检测精度。
Beam-Wise Statistical Background Subtraction for Static Roadside LiDAR: A Cross-Sensor Benchmark Study
- 按激光束独立建模背景，结合角度与三维空间滤波。
- 在多场景异构传感器上实现高精度、实时背景剔除，召回率超95%。
- 开源数据集与代码，适合雷达感知研究者复现与应用。
背景剔除是基于基础设施的激光雷达感知的关键预处理步骤，可无需语义标注高效分离动态交通参与者。然而，针对固定安装激光雷达的系统性跨传感器评估与可复现研究仍属空白。本文提出一种面向静止路边激光雷达的分束统计背景剔除对比基准。将背景估计建模为每束激光的时间序列问题，探究能捕捉主导及多模态背景结构的互补统计策略，并结合角向与三维空间滤波。为支持可复现评估，引入HighwayScene新数据集，该数据集在静态路边设置下采集，同时扩展公开的CoopScenes数据集，增加点级静态/动态标注。在多个场景及异构传感技术下，证明分束统计建模具有鲁棒性和可迁移性。结合轻量级每束模型与空间一致性滤波，显著提升精度，同时保持高召回率和实时能力。所有数据集、标注与实现均已公开。
原文摘要 · Abstract (English)
Background subtraction is a key preprocessing step for infrastructure-based LiDAR perception, enabling efficient isolation of dynamic traffic participants without semantic annotations. However, systematic cross-sensor evaluations and reproducible studies for static roadside LiDAR are missing. This paper presents a comparative benchmark of beam-wise statistical background subtraction for statically mounted LiDAR sensors. We formulate background estimation as a per-beam temporal modeling problem and investigate complementary statistical strategies that capture dominant as well as multi-modal background structures, combined with spatial filtering in the angular and 3D domain. To enable reproducible evaluation, we introduce HighwayScene, a new multi-LiDAR dataset recorded in a static roadside setup, and extend the public CoopScenes dataset with static/dynamic point-wise annotations. Across multiple scenes and heterogeneous sensing technologies, we demonstrate that beam-wise statistical modeling provides a robust and transferable solution. Combining lightweight per-beam models with spatial consistency filtering substantially improves precision while maintaining high recall and real-time capability. All datasets, annotations, and implementations are publicly released.
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