面向静态路侧激光雷达的波束级统计背景减除：跨传感器基准研究
Beam-Wise Statistical Background Subtraction for Static Roadside LiDAR: A Cross-Sensor Benchmark Study
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中文总结 AI 辅助
本文针对静态路侧激光雷达缺失系统性跨传感器评估的问题，构建波束级统计背景减除基准，引入新数据集并结合空间滤波，实现了鲁棒可迁移的背景减除方案，提升了精度并保持高召回率与实时性，相关资源已公开。
中文摘要 AI 辅助
背景减除是基于基础设施的激光雷达感知的关键预处理步骤，无需语义标注即可高效分离动态交通参与者。然而，针对静态路侧激光雷达的系统性跨传感器评估与可复现研究仍处于空白。本文提出静态安装激光雷达的波束级统计背景减除的对比基准。我们将背景估计建模为逐波束时间序列问题，研究捕捉主导及多模态背景结构的互补统计策略，结合角度与三维域的空间滤波。为实现可复现评估，我们引入在静态路侧场景录制的多激光雷达数据集HighwayScene，并在公开CoopScenes数据集上补充静态/动态逐点标注。在多个场景与异构传感技术上，我们证明波束级统计建模提供了鲁棒且可迁移的解决方案；将轻量逐波束模型与空间一致性滤波结合，在维持高召回率与实时性的同时大幅提升精度。所有数据集、标注与实现均已公开。
英文摘要
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.
发表机构
- University of Applied Sciences Esslingen（埃斯林根应用科学大学）
- Faculty of Computer Science and Engineering（计算机科学与工程学院）
- Institute for Intelligent Systems（智能系统研究所）
机构由 AI 辅助整理，请以论文原文为准。
