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Dino in the room: Leveraging 2D foundation models for 3D segmentation. arXiv preprint arXiv: 2503.18944."},{"key":"10.1016\/j.eswa.2026.131832_bib0071","series-title":"Proceedings of the 32nd ACM international conference on multimedia","first-page":"1505","article-title":"Mambamos: LiDAR-based 3D moving object segmentation with motion-aware state space model","author":"Zeng","year":"2024"},{"key":"10.1016\/j.eswa.2026.131832_bib0072","series-title":"European conference on computer vision","first-page":"644","article-title":"Deep fusionnet for point cloud semantic segmentation","author":"Zhang","year":"2020"},{"key":"10.1016\/j.eswa.2026.131832_bib0073","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122716","article-title":"Mmaf-net: Multi-view multi-stage adaptive fusion for multi-sensor 3D object detection","volume":"242","author":"Zhang","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.131832_bib0074","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"9601","article-title":"Polarnet: An improved grid representation for online LiDAR point clouds semantic segmentation","author":"Zhang","year":"2020"},{"issue":"18","key":"10.1016\/j.eswa.2026.131832_bib0075","doi-asserted-by":"crossref","first-page":"4471","DOI":"10.3390\/rs14184471","article-title":"Svaseg: Sparse voxel-based attention for 3D LiDAR point cloud semantic segmentation","volume":"14","author":"Zhao","year":"2022","journal-title":"Remote Sensing"},{"key":"10.1016\/j.eswa.2026.131832_bib0076","series-title":"2021 IEEE\/RSJ international conference on intelligent robots and systems (IROS)","first-page":"4453","article-title":"Fidnet: LiDAR point cloud semantic segmentation with fully interpolation decoding","author":"Zhao","year":"2021"},{"key":"10.1016\/j.eswa.2026.131832_bib0077","series-title":"European conference on computer vision","first-page":"695","article-title":"Number-adaptive prototype learning for 3D point cloud semantic segmentation","author":"Zhao","year":"2022"},{"key":"10.1016\/j.eswa.2026.131832_bib0078","series-title":"International conference on machine learning","article-title":"Vision mamba: Efficient visual representation learning with bidirectional state space model","author":"Zhu","year":"2024"},{"key":"10.1016\/j.eswa.2026.131832_bib0079","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"9939","article-title":"Cylindrical and asymmetrical 3D convolution networks for LiDAR segmentation","author":"Zhu","year":"2021"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426007451?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426007451?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T17:46:44Z","timestamp":1774028804000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426007451"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":79,"alternative-id":["S0957417426007451"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.131832","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"HTMNet: A hybrid transformer-mamba network for LiDAR-based 3D detection and semantic segmentation","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.131832","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. 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