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Improved Density Clustering for Spacing Measurement of Irregular Rebar Mesh from 3D Point Clouds

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成果类型:
期刊论文
作者:
Li, Fengling;Long, Zezhou;Hu, Hongwei;Gao, Kai
通讯作者:
Li, FL
作者机构:
[Li, Fengling] Changsha Univ Sci & Technol, Coll Mech & Vehicle Engn, Changsha 410114, Peoples R China.
[Long, Zezhou; Hu, Hongwei; Gao, Kai] Changsha Univ Sci & Technol, Coll Automobile & Mech Engn, Changsha 410114, Peoples R China.
通讯机构:
[Li, FL ] C
Changsha Univ Sci & Technol, Coll Mech & Vehicle Engn, Changsha 410114, Peoples R China.
语种:
英文
关键词:
Computer networks;Project management;Quality control;Reinforcing steel;Spacing;Structural safety;Verification
期刊:
JOURNAL OF SURVEYING ENGINEERING
ISSN:
0733-9453
年:
2025
卷:
151
期:
3
页码:
04025005
基金类别:
Natural Science Foundation of Hunan Province [2023JJ50237, 2023JJ60546]
机构署名:
本校为第一且通讯机构
院系归属:
汽车与机械工程学院
摘要:
Proper spacing of rebar mesh is essential for structural integrity and safety, but measuring the spacing performance in three-dimensional (3D) space can be complicated. Existing spacing verification either relies on well-annotated data or encounters challenges when dealing with obscured point clouds. To address these challenges, we propose a novel approach using a projection clustering algorithm. Initially, we project the 3D point cloud onto a frontal view and perform density peak clustering in two directions, enabling accurate recognition of vertical and horizontal boundaries. Then, we iterat...

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