Three-Dimensional Point Cloud Denoising for Tunnel Data by Combining Intensity and Geometry Information

Yan Bao, Yucheng Wen, Chao Tang, Zhe Sun (), Xiaolin Meng, Dongliang Zhang and Li Wang
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Yan Bao: The Key Laboratory of Urban Security and Disaster Engineering of China Ministry of Education, Beijing University of Technology, Beijing 100124, China
Yucheng Wen: The Key Laboratory of Urban Security and Disaster Engineering of China Ministry of Education, Beijing University of Technology, Beijing 100124, China
Chao Tang: Beijing Urban Construction Exploration & Surveying Design Research Institute Co., Ltd., Beijing 100101, China
Zhe Sun: The Key Laboratory of Urban Security and Disaster Engineering of China Ministry of Education, Beijing University of Technology, Beijing 100124, China
Xiaolin Meng: Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
Dongliang Zhang: The Key Laboratory of Urban Security and Disaster Engineering of China Ministry of Education, Beijing University of Technology, Beijing 100124, China
Li Wang: The Key Laboratory of Urban Security and Disaster Engineering of China Ministry of Education, Beijing University of Technology, Beijing 100124, China

Sustainability, 2024, vol. 16, issue 5, 1-21

Abstract: At present, three-dimensional laser scanners are used to scan subway shield tunnels and generate point cloud data as the basis for extracting a variety of information about tunnel defects. However, there are obstacles in the tunnel such as pipelines, tracks, and signaling systems that cause noise in the point cloud. Usually, the data of the tunnel point cloud are huge, and the efficiency of artificial denoising is low. Faced with this problem, based on the respective characteristics of the geometric shape and reflection intensity of the tunnel point cloud and their correlation, this paper proposes a tunnel point cloud denoising method. The method includes the following three parts: reflection intensity threshold denoising, joint shape and reflection intensity denoising, and shape denoising. Through the experiment on the single-ring segment point cloud of a shield tunnel, the method proposed in this paper takes 2 min to remove 99.77% of the noise in the point cloud. Compared with manual denoising, the method proposed in this paper takes two fifteenths of the time to achieve the same denoising effect. The method proposed in this paper meets the requirements of a tunnel point cloud data survey. Thus, it provides support for the efficient, accurate, and automatic daily maintenance and surveys of tunnels.

Keywords: shield tunnel; point cloud data; point cloud denoising; point cloud segmentation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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