{"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/online-segmentation-of-lidar-sequences","title":"Online Segmentation of LiDAR Sequences: Dataset and Algorithm","arxiv_id":"2206.08194","date":"2022-06-16","proceeding":null,"authors":["Romain Loiseau","Mathieu Aubry","Loïc Landrieu"],"abstract":"Roof-mounted spinning LiDAR sensors are widely used by autonomous vehicles. However, most semantic datasets and algorithms used for LiDAR sequence segmentation operate on $360^\\circ$ frames, causing an acquisition latency incompatible with real-time applications. To address this issue, we first introduce HelixNet, a $10$ billion point dataset with fine-grained labels, timestamps, and sensor rotation information necessary to accurately assess the real-time readiness of segmentation algorithms. Second, we propose Helix4D, a compact and efficient spatio-temporal transformer architecture specifically designed for rotating LiDAR sequences. Helix4D operates on acquisition slices corresponding to a fraction of a full sensor rotation, significantly reducing the total latency. Helix4D reaches accuracy on par with the best segmentation algorithms on HelixNet and SemanticKITTI with a reduction of over $5\\times$ in terms of latency and $50\\times$ in model size. The code and data are available at: https://romainloiseau.fr/helixnet","url_abs":"https://arxiv.org/abs/2206.08194v2","url_pdf":"https://arxiv.org/pdf/2206.08194v2.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":"online-segmentation-of-lidar-sequences","repo_url":"https://github.com/romainloiseau/Helix4D","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"autonomous-vehicles","task_name":"Autonomous Vehicles"},{"task_slug":"lidar-semantic-segmentation","task_name":"LIDAR Semantic Segmentation"},{"task_slug":"real-time-semantic-segmentation","task_name":"Real-Time Semantic Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[{"slug":"helixnet","name":"HelixNet","full_name":"HelixNet: A Dataset for Online LiDAR Segmentation"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/real-time-semantic-segmentation-on-helixnet","task":"Real-Time Semantic Segmentation","dataset":"HelixNet","model":"Helix4D","rank_in_archive_order":1,"of":1,"metrics":{"Inference Time (ms) (1/5 rotation)":"19","mIoU (1/5 rotation)":"78.7"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2206.08194","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}