Structural Analysis and 3D Reconstruction of Underground Pipeline Systems Based on LiDAR Point Clouds

作     者：Lai, Qiuyao Xin, Qinchuan Tian, Yuhang Chen, Xiaoyou Li, Yujie Wu, Ruohan 

作者机构：Sun Yat Sen Univ Sch Geog & Planning Guangzhou 510275 Peoples R China 

出 版 物：《REMOTE SENSING》 (Remote Sens.)

年 卷 期：2025年第17卷第2期

页      面：341-341页

核心收录：

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 1002[医学-临床医学] 070801[理学-固体地球物理学] 07[理学] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术] 

基　　金：National Natural Science Foundation of China [42371483, 42401573] Postdoctoral FellowshipProgram of CPSF [GZB20240880] Project of Underground Utilities Recognition and Extraction Service Based on Deep Learning of Point Cloud [GHK(2022)K003B01] 

主　　题：underground pipeline 3D reconstruction point clouds building information model RANSAC algorithm 

摘      要：The underground pipeline is a critical component of urban water supply and drainage infrastructure. However, the absence of accurate pipe information frequently leads to construction delays and cost overruns, adversely impacting urban management and economic development. To address these challenges, the digital management of underground pipelines has become essential. Despite its importance, research on the structural analysis and reconstruction of underground pipelines remains limited, primarily due to the complexity of underground environments and the technical constraints of LiDAR technology. This study proposes a framework for reconstructing underground pipelines based on unstructured point cloud data, aiming to accurately identify and reconstruct pipe structures from complex scenes. The Random Sample Consensus (RANSAC) algorithm, enhanced with parameter-adaptive adjustments and subset-independent fitting strategies, is employed to fit centerline segments from the set of center points. These segments were used to reconstruct topological connections, and a Building Information Model (BIM) of the underground pipeline was generated based on the structural analysis. Experiments on actual underground scenes evaluated the method using recall rate, radius error, and deviation between point clouds and models. Results showed an 88.8% recall rate, an average relative radius error below 3%, and a deviation of 3.79 cm, demonstrating the framework s accuracy. This research provides crucial support for pipeline management and planning in smart city development.