{"ID":2850360,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.22390","arxiv_id":"2510.22390","title":"A Fully Interpretable Statistical Approach for Roadside LiDAR Background Subtraction","abstract":"We present a fully interpretable and flexible statistical method for background subtraction in roadside LiDAR data, aimed at enhancing infrastructure-based perception in automated driving. Our approach introduces both a Gaussian distribution grid (GDG), which models the spatial statistics of the background using background-only scans, and a filtering algorithm that uses this representation to classify LiDAR points as foreground or background. The method supports diverse LiDAR types, including multiline 360 degree and micro-electro-mechanical systems (MEMS) sensors, and adapts to various configurations. Evaluated on the publicly available RCooper dataset, it outperforms state-of-the-art techniques in accuracy and flexibility, even with minimal background data. Its efficient implementation ensures reliable performance on low-resource hardware, enabling scalable real-world deployment.","short_abstract":"We present a fully interpretable and flexible statistical method for background subtraction in roadside LiDAR data, aimed at enhancing infrastructure-based perception in automated driving. Our approach introduces both a Gaussian distribution grid (GDG), which models the spatial statistics of the background using backgr...","url_abs":"https://arxiv.org/abs/2510.22390","url_pdf":"https://arxiv.org/pdf/2510.22390v2","authors":"[\"Aitor Iglesias\",\"Nerea Aranjuelo\",\"Patricia Javierre\",\"Ainhoa Menendez\",\"Ignacio Arganda-Carreras\",\"Marcos Nieto\"]","published":"2025-10-25T18:18:10Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}