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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yan Bao & Yucheng Wen & Chao Tang & Zhe Sun & Xiaolin Meng & Dongliang Zhang & Li Wang, 2024. "Three-Dimensional Point Cloud Denoising for Tunnel Data by Combining Intensity and Geometry Information," Sustainability, MDPI, vol. 16(5), pages 1-21, March.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:5:p:2077-:d:1349967
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