{"id":32621,"date":"2026-01-21T17:02:19","date_gmt":"2026-01-21T17:02:19","guid":{"rendered":"https:\/\/lamarr-institute.org\/publication\/tfnet-exploiting-temporal-cues-for-fast-and-accurate-lidar-semantic-segmentation\/"},"modified":"2026-06-08T13:21:15","modified_gmt":"2026-06-08T13:21:15","slug":"tfnet-exploiting-temporal-cues-for-fast-and-accurate-lidar-semantic-segmentation","status":"publish","type":"publication","link":"https:\/\/lamarr-institute.org\/de\/publication\/tfnet-exploiting-temporal-cues-for-fast-and-accurate-lidar-semantic-segmentation\/","title":{"rendered":"Tfnet: Exploiting temporal cues for fast and accurate lidar semantic segmentation"},"content":{"rendered":"<p>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 &#8222;many-to-one&#8220; problem caused by the range image&#8217;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 &#8222;many-to-one&#8220; 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.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>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 &#8222;many-to-one&#8220; problem caused by the range image&#8217;s limited horizontal and vertical [&hellip;]<\/p>\n","protected":false},"author":12,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[32],"class_list":["post-32621","publication","type-publication","status-publish","hentry","publication-type-inproceedings"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32621","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication"}],"about":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/types\/publication"}],"author":[{"embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/users\/12"}],"version-history":[{"count":0,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32621\/revisions"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=32621"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=32621"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}