{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T13:54:24Z","timestamp":1770990864475,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T00:00:00Z","timestamp":1703462400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program","award":["2022YFC3004701"],"award-info":[{"award-number":["2022YFC3004701"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent years, point cloud segmentation technology has increasingly played a pivotal role in tunnel construction and maintenance. Currently, traditional methods for segmenting point clouds in tunnel scenes often rely on a multitude of attribute information, including spatial distribution, color, normal vectors, intensity, and density. However, the underground tunnel scenes show greater complexity than road tunnel scenes, such as dim light, indistinct boundaries of tunnel walls, and disordered pipelines. Furthermore, issues pertaining to data quality, such as the lack of color information and insufficient annotated data, contribute to the subpar performance of conventional point cloud segmentation algorithms. To address this issue, a 3D point cloud segmentation framework specifically for underground tunnels is proposed based on the Segment Anything Model (SAM). This framework effectively leverages the generalization capability of the visual foundation model to automatically adapt to various scenes and perform efficient segmentation of tunnel point clouds. Specifically, the tunnel is first sliced along its direction on the tunnel line. Then, each sliced point cloud is projected onto a two-dimensional plane. Various projection methods and point cloud coloring techniques are employed to enhance SAM\u2019s segmentation performance in images. Finally, the semantic segmentation of the entire underground tunnel is achieved by a small set of manually annotated semantic labels used as prompts in a progressive and recursive manner. The key feature of this method lies in its independence from model training, as it directly and efficiently addresses tunnel point cloud segmentation challenges by capitalizing on the generalization capability of foundation model. Comparative experiments against classical region growing algorithms and PointNet++ deep learning algorithms demonstrate the superior performance of our proposed algorithm.<\/jats:p>","DOI":"10.3390\/rs16010097","type":"journal-article","created":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T23:00:12Z","timestamp":1703545212000},"page":"97","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["A Point Cloud Segmentation Method for Dim and Cluttered Underground Tunnel Scenes Based on the Segment Anything Model"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-2596-5611","authenticated-orcid":false,"given":"Jitong","family":"Kang","sequence":"first","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9280-975X","authenticated-orcid":false,"given":"Ning","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4763-1817","authenticated-orcid":false,"given":"Mei","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China"}]},{"given":"Shanjun","family":"Mao","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7978-8045","authenticated-orcid":false,"given":"Haoyuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China"}]},{"given":"Yingbo","family":"Fan","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6560-193X","authenticated-orcid":false,"given":"Hui","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1007\/s10064-015-0748-3","article-title":"Mapping and Characterization of Rock Discontinuities in a Tunnel Using 3D Terrestrial Laser Scanning","volume":"75","author":"Cacciari","year":"2016","journal-title":"Bull. 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