{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T18:01:41Z","timestamp":1784916101166,"version":"3.55.0"},"reference-count":61,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T00:00:00Z","timestamp":1784851200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Natural Science Research Project in Colleges and Universities of An-hui Province","award":["2023AH051592"],"award-info":[{"award-number":["2023AH051592"]}]},{"name":"the College Students Innovative Entrepreneurial Training Plan Program","award":["S202510377005, S202510377007"],"award-info":[{"award-number":["S202510377005, S202510377007"]}]},{"DOI":"10.13039\/501100013286","name":"Specialized Research Fund for the Doctoral Program of Higher Education of China","doi-asserted-by":"publisher","award":["2025AHGXZK31295"],"award-info":[{"award-number":["2025AHGXZK31295"]}],"id":[{"id":"10.13039\/501100013286","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Current point cloud simplification methods for complex ground objects face a persistent challenge: balancing noise robustness and geometric detail preservation. To resolve this, we present a lightweight simplification framework that integrates multi-scale adaptive filtering, entropy-driven spatial partitioning, and an enhanced medial axis transform (MAT). This framework incorporates three targeted technical innovations: (1) An adaptive sliding window polynomial fitting filter with multi-resolution weight adjustment, which achieves coordinated noise suppression and sharp feature preservation; (2) A curvature-weighted enhanced MAT algorithm that reduces skeletal artifacts and topological fractures; (3) An entropy-driven adaptive recursive axis-aligned bounding box (AABB) partitioning strategy, which mitigates the inherent trade-off of conventional uniform partitioning: memory waste in sparse regions and feature loss in dense areas. We validated this framework using self-collected datasets of buildings, vegetation, and roads, and further verified its generalization performance on the public STPLS3D benchmark. Our method achieves an average noise removal rate of 87.76%, representing an average improvement of 11.99% over the baseline method; edge retention is 83.3%, an average improvement of 7.35%; the topological integrity and branch accuracy of the skeleton extraction reached 0.93 and 0.95, respectively, both of which were the best among the tested algorithms; the mean error in normal estimation was as low as 3.34%, and the average point-to-surface distance and fracture rate in 3D reconstruction were the lowest. This method provides a point cloud processing solution that combines accuracy and efficiency for fields such as 3D geographic information modeling and scene reconstruction.<\/jats:p>","DOI":"10.1371\/journal.pone.0353953","type":"journal-article","created":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T17:25:31Z","timestamp":1784913931000},"page":"e0353953","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Robust point cloud lightweighting with multi-scale adaptive filtering and entropy-driven subdivision"],"prefix":"10.1371","volume":"21","author":[{"given":"Weibo","family":"Zeng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9638-3437","authenticated-orcid":true,"given":"Xinyu","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengchan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjing","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,7,24]]},"reference":[{"issue":"04","key":"pone.0353953.ref001","first-page":"269","article-title":"Multi-Feature 3D Road Point Cloud Semantic Segmentation Method Based on Convolutional Neural Network","volume":"47","author":"A Zhang","year":"2020","journal-title":"Chinese Journal of Lasers"},{"issue":"3","key":"pone.0353953.ref002","first-page":"891","article-title":"Application of simulated annealing method and neural network on optimizing soil sampling schemes based on road distribution","volume":"26","author":"Z Han","year":"2015","journal-title":"Ying Yong Sheng Tai Xue Bao"},{"issue":"20","key":"pone.0353953.ref003","first-page":"134","article-title":"Adaptive detection of harmonic current in power grid based on improved cascade neural network","volume":"44","author":"C Liu","year":"2016","journal-title":"Power System Protection and Control"},{"issue":"2","key":"pone.0353953.ref004","first-page":"177","article-title":"Classification and segmentation of defect carrots using deep multi-branch models fusion network","volume":"37","author":"n Xie W e i j u","year":"2021","journal-title":"Trans CSAE"},{"key":"pone.0353953.ref005","doi-asserted-by":"crossref","unstructured":"Li J, Chen BM, Lee GH. 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