{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/forestlpr-lidar-place-recognition-in-forests","title":"ForestLPR: LiDAR Place Recognition in Forests Attentioning Multiple BEV Density Images","arxiv_id":"2503.04475","date":"2025-03-06","proceeding":"CVPR 2025 1","authors":["Yanqing Shen","Turcan Tuna","Marco Hutter","Cesar Cadena","Nanning Zheng"],"abstract":"Place recognition is essential to maintain global consistency in large-scale localization systems. While research in urban environments has progressed significantly using LiDARs or cameras, applications in natural forest-like environments remain largely under-explored. Furthermore, forests present particular challenges due to high self-similarity and substantial variations in vegetation growth over time. In this work, we propose a robust LiDAR-based place recognition method for natural forests, ForestLPR. We hypothesize that a set of cross-sectional images of the forest's geometry at different heights contains the information needed to recognize revisiting a place. The cross-sectional images are represented by \\ac{bev} density images of horizontal slices of the point cloud at different heights. Our approach utilizes a visual transformer as the shared backbone to produce sets of local descriptors and introduces a multi-BEV interaction module to attend to information at different heights adaptively. It is followed by an aggregation layer that produces a rotation-invariant place descriptor. We evaluated the efficacy of our method extensively on real-world data from public benchmarks as well as robotic datasets and compared it against the state-of-the-art (SOTA) methods. The results indicate that ForestLPR has consistently good performance on all evaluations and achieves an average increase of 7.38\\% and 9.11\\% on Recall@1 over the closest competitor on intra-sequence loop closure detection and inter-sequence re-localization, respectively, validating our hypothesis","url_abs":"https://arxiv.org/abs/2503.04475v1","url_pdf":"https://arxiv.org/pdf/2503.04475v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"forestlpr-lidar-place-recognition-in-forests","repo_url":"https://github.com/shenyanqing1105/ForestLPR-CVPR2025","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"3d-place-recognition","task_name":"3D Place Recognition"},{"task_slug":"loop-closure-detection","task_name":"Loop Closure Detection"}],"methods":[{"method_slug":"set","method_name":"SET"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-place-recognition-on-wild-places","task":"3D Place Recognition","dataset":"Wild-Places","model":"ForestLPR","rank_in_archive_order":1,"of":4,"metrics":{"AR@1 (Intra-Seq)":"77.62","AR@1 Inter-Seq":"78.73"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2503.04475","atlas_url":"https://app.syntology.ai/?focus=2503.04475","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}