{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T11:17:34Z","timestamp":1784805454064,"version":"3.55.0"},"reference-count":68,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T00:00:00Z","timestamp":1772582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003819","name":"Natural Science Foundation of Hubei Province","doi-asserted-by":"publisher","award":["2025AFB341"],"award-info":[{"award-number":["2025AFB341"]}],"id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Sichuan Science and Technology Program","award":["2025ZNSFSC0529"],"award-info":[{"award-number":["2025ZNSFSC0529"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62301373"],"award-info":[{"award-number":["62301373"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Open Fund of Hubei Engineering Research Center for High-Precision Deformation Monitoring with \u201cBeidou + Cloud\u201d","award":["HBBDGJ202508Y"],"award-info":[{"award-number":["HBBDGJ202508Y"]}]},{"name":"Joint Open Fund of the Research Platforms of School of Computer Science, China University of Geosciences, Wuhan","award":["PTLH2024-B-11"],"award-info":[{"award-number":["PTLH2024-B-11"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Indoor 3D point cloud semantic segmentation is a fundamental task for fine-grained scene understanding and intelligent perception. Due to the prohibitive cost of dense point-wise annotations, weakly supervised learning has emerged as a promising alternative for indoor point cloud segmentation. However, existing weakly supervised methods commonly rely on fixed confidence thresholds for pseudo-label selection, which exhibit limited generalization caused by threshold sensitivity, underutilization of informative low-confidence regions, and progressive noise accumulation during self-training. To address these issues, we propose TGR-T, a weakly supervised framework for indoor 3D point cloud semantic segmentation that incorporates truncated-Gaussian-weighted reliability with adaptive dynamic thresholding. Specifically, a reliability-adaptive dynamic thresholding strategy is introduced to guide pseudo-label selection based on the evolving confidence statistics of unlabeled mini-batches, with exponential moving average smoothing employed to produce stable global estimates and robust separation of reliable and ambiguous regions. To further exploit uncertain regions, a learnable truncated Gaussian weighting function is designed to explicitly model prediction uncertainty within the ambiguous set, providing soft supervision by assigning adaptive weights to low-confidence predictions during optimization. Extensive experimental results demonstrate that the proposed framework significantly enhances the exploitation of unlabeled data under extremely limited supervision: extensive experiments conducted on standard indoor 3D scene benchmarks demonstrate that TGR-T achieves competitive or superior segmentation performance under extremely sparse supervision and can even outperform several fully supervised baselines trained with dense annotations while using only 1% labeled points, thereby substantially narrowing the performance gap between weakly supervised and fully supervised 3D semantic segmentation methods.<\/jats:p>","DOI":"10.3390\/ijgi15030108","type":"journal-article","created":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T13:11:36Z","timestamp":1772629896000},"page":"108","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["TGR-T: Truncated-Gaussian-Weighted Reliability for Adaptive Dynamic Thresholding in Weakly Supervised Indoor 3D Point Cloud Segmentation"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6648-2768","authenticated-orcid":false,"given":"Ziwei","family":"Luo","sequence":"first","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan 430200, China"},{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyue","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan 430200, China"},{"name":"Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software, Ministry of Education, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan 430200, China"},{"name":"Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanyu","family":"Qi","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan 430200, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2375-3332","authenticated-orcid":false,"given":"Chen","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan 430200, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhong","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5258-1667","authenticated-orcid":false,"given":"Tao","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering, Sichuan University, Chengdu 610065, China"},{"name":"Chengdu Qianjia Technology Co., Ltd., Chengdu 610207, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Riz, L., Saltori, C., Ricci, E., and Poiesi, F. 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