{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T12:32:14Z","timestamp":1766579534483,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T00:00:00Z","timestamp":1752710400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2024YFB3909604","42471446","2023KFJJ10"],"award-info":[{"award-number":["2024YFB3909604","42471446","2023KFJJ10"]}]},{"name":"National Natural Science Foundation of China","award":["2024YFB3909604","42471446","2023KFJJ10"],"award-info":[{"award-number":["2024YFB3909604","42471446","2023KFJJ10"]}]},{"name":"Open Fund of National Engineering Research Center of Geographic Information System","award":["2024YFB3909604","42471446","2023KFJJ10"],"award-info":[{"award-number":["2024YFB3909604","42471446","2023KFJJ10"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Accurate segmentation of point clouds into categories such as roads, buildings, and trees is critical for applications in 3D reconstruction and autonomous driving. However, large-scale point cloud segmentation encounters challenges such as uneven density distribution, inefficient sampling, and limited feature extraction capabilities. To address these issues, this paper proposes RT-Net, a novel framework that incorporates a density-based grid decimation algorithm for efficient preprocessing of outdoor point clouds. The proposed framework helps alleviate the problem of uneven density distribution and improves computational efficiency. RT-Net also introduces two modules: Local Attention Aggregation, which extracts local detailed features of points using an attention mechanism, enhancing the model\u2019s recognition ability for small-sized objects; and Attention Residual, which integrates local details of point clouds with global features by an attention mechanism to improve the model\u2019s generalization ability. Experimental results on the Toronto3D, Semantic3D, and SemanticKITTI datasets demonstrate the superiority of RT-Net for small-sized object segmentation, achieving state-of-the-art mean Intersection over Union (mIoU) scores of 86.79% on Toronto3D and 79.88% on Semantic3D.<\/jats:p>","DOI":"10.3390\/ijgi14070279","type":"journal-article","created":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T15:15:54Z","timestamp":1752765354000},"page":"279","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Large-Scale Point Cloud Semantic Segmentation with Density-Based Grid Decimation"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4994-7644","authenticated-orcid":false,"given":"Liangcun","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8345-9555","authenticated-orcid":false,"given":"Jiacheng","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5464-9209","authenticated-orcid":false,"given":"Han","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China"}]},{"given":"Boyi","family":"Shangguan","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Space-Ground Integrated Information Technology, Space Star Technology Co., Ltd., Beijing 100095, China"}]},{"given":"Hongyu","family":"Xiao","sequence":"additional","affiliation":[{"name":"Changjiang Schinta Software Technology Co., Ltd., Wuhan 430010, China"}]},{"given":"Zeqiang","family":"Chen","sequence":"additional","affiliation":[{"name":"The National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Charles, R.Q., Su, H., Kaichun, M., and Guibas, L.J. 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