{"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/tfnet-exploiting-temporal-cues-for-fast-and","title":"TFNet: Exploiting Temporal Cues for Fast and Accurate LiDAR Semantic Segmentation","arxiv_id":"2309.07849","date":"2023-09-14","proceeding":null,"authors":["Rong Li","Shijie Li","Xieyuanli Chen","Teli Ma","Juergen Gall","Junwei Liang"],"abstract":"LiDAR semantic segmentation plays a crucial role in enabling autonomous driving and robots to understand their surroundings accurately and robustly. A multitude of methods exist within this domain, including point-based, range-image-based, polar-coordinate-based, and hybrid strategies. Among these, range-image-based techniques have gained widespread adoption in practical applications due to their efficiency. However, they face a significant challenge known as the ``many-to-one'' problem caused by the range image's limited horizontal and vertical angular resolution. As a result, around 20% of the 3D points can be occluded. In this paper, we present TFNet, a range-image-based LiDAR semantic segmentation method that utilizes temporal information to address this issue. Specifically, we incorporate a temporal fusion layer to extract useful information from previous scans and integrate it with the current scan. We then design a max-voting-based post-processing technique to correct false predictions, particularly those caused by the ``many-to-one'' issue. We evaluated the approach on two benchmarks and demonstrated that the plug-in post-processing technique is generic and can be applied to various networks.","url_abs":"https://arxiv.org/abs/2309.07849v3","url_pdf":"https://arxiv.org/pdf/2309.07849v3.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":[],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"lidar-semantic-segmentation","task_name":"LIDAR Semantic Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/lidar-semantic-segmentation-on-semantickitti","task":"LIDAR Semantic Segmentation","dataset":"SemanticKITTI","model":"TFNet","rank_in_archive_order":3,"of":4,"metrics":{"mIOU":"66.1%"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-semanticposs","task":"Semantic Segmentation","dataset":"SemanticPOSS","model":"TFNet","rank_in_archive_order":1,"of":1,"metrics":{"Mean IoU":"51.9"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2309.07849","atlas_url":"https://app.syntology.ai/?focus=2309.07849","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}