Beam-Wise Statistical Background Subtraction for Static Roadside LiDAR: A Cross-Sensor Benchmark Study

2026-08-14

AI Summary

This study addresses the lack of cross-sensor background subtraction evaluation for static roadside LiDAR by proposing a per-beam statistical modeling and angle-spatial joint filtering approach that enables efficient dynamic perception without semantic annotations. We introduce the HighwayScene dataset and extend CoopScenes with point-level dynamic-static annotations to establish the first reproducible cross-sensor benchmark. Experimental results demonstrate the method’s robustness and transferability across heterogeneous scenarios, significantly improving detection accuracy while maintaining real-time performance. By open-sourcing all data and code, this work fills a critical gap in systematic evaluation for roadside LiDAR-based dynamic object detection in intelligent transportation systems.

Abstract

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.

Problem

• Background Subtraction
• Static Roadside LiDAR
• Cross-Sensor Benchmark
• Infrastructure-based Perception

Innovation

• Beam-wise statistical modeling
• Background subtraction
• Static roadside LiDAR
• HighwayScene dataset
• Cross-sensor benchmark