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

arXiv: https://arxiv.org/abs/2608.14868 · HTML: https://mlsanity.com/paper/2608.14868/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 14 Aug 2026.

## Builds on (most cited first, 5 of 5)

- [Roadside Lidar Vehicle Detection and Tracking Using Range And Intensity Background Subtraction](https://mlsanity.com/paper/2201.04756/) (2022)
- [STAL3D: Unsupervised Domain Adaptation for 3D Object Detection via Collaborating Self-Training and Adversarial Learning](https://mlsanity.com/paper/2406.19362/) (2024)
- [CoopScenes: Multi-Scene Infrastructure and Vehicle Data for Advancing Collective Perception in Autonomous Driving](https://mlsanity.com/paper/2407.08261/) (2024)
- [Deep Generic Dynamic Object Detection Based on Dynamic Grid Maps](https://mlsanity.com/paper/2410.14799/) (2024)
- [A Fully Interpretable Statistical Approach for Roadside LiDAR Background Subtraction](https://mlsanity.com/paper/2510.22390/) (2025)

## Abstract (arXiv)

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.