{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T19:45:43Z","timestamp":1785872743650,"version":"3.56.0"},"reference-count":45,"publisher":"Optica Publishing Group","issue":"23","license":[{"start":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T00:00:00Z","timestamp":1785801600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/doi.org\/10.1364\/OA_License_v2#VOR"},{"start":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T00:00:00Z","timestamp":1785801600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/opg.optica.org\/policies\/opg-tdm-policy.json"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62371081"],"award-info":[{"award-number":["62371081"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"},{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":["opg.optica.org"],"crossmark-restriction":false},"short-container-title":["Appl. Opt."],"published-print":{"date-parts":[[2026,8,10]]},"abstract":"<jats:p>LiDAR semantic segmentation is significant in applications such as autonomous driving and robot navigation, as it greatly improves scene perception and object detection. However, the existing methods face the challenges of achieving high segmentation accuracy while maintaining low computational cost and complexity. In this paper, we propose a new, to our knowledge, efficient and accurate semantic segmentation network for LiDAR, called Range-FDSeg. To reduce the risk of information compression and loss when projecting 3D point cloud data onto 2D range images, we design a multi-channel fusion interactive learning (FIL) module. This module effectively integrates multimodal channels, such as coordinates, depth, and reflectivity, for interactive learning. As a result, FIL module can reduce the noise interference inherent in individual channels and capture the underlying relationships between different physical quantities. To further improve the performance, we introduce a lightweight and dynamic upsampler, called Dysample-S+. It effectively resolves the inherent challenges of traditional sampling methods through its adaptive weighting mechanism, which dynamically adjusts to local geometric patterns and density variations in raw point clouds. Extensive evaluations on publicly available benchmark datasets, including SemanticKITTI, SemanticPOSS, and NuScenes, demonstrate that the proposed Range-FDSeg outperforms most existing state-of-the-art methods.<\/jats:p>","DOI":"10.1364\/ao.590690","type":"journal-article","created":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T14:00:08Z","timestamp":1784124008000},"page":"7791","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Range-FDSeg: LiDAR semantic segmentation based on fusion interactive learning and dynamic sampling for autonomous driving scenarios"],"prefix":"10.1364","volume":"65","author":[{"given":"Honglin","family":"Tan","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04vgbd477","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3921-9152","authenticated-orcid":true,"given":"Fen","family":"Chen","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04vgbd477","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingna","family":"Liu","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04vgbd477","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sisi","family":"Zhang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04vgbd477","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lian","family":"Huang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04vgbd477","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8286-538X","authenticated-orcid":true,"given":"Zongju","family":"Peng","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04vgbd477","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2026,8,4]]},"reference":[{"key":"ao-65-23-7791-R1","doi-asserted-by":"publisher","first-page":"8578","DOI":"10.1109\/TVT.2021.3100264","type":"journal-article","volume":"70","author":"Liao","year":"2021","journal-title":"IEEE Trans. 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All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.","name":"copyright","label":"Copyright"}]}}