System Design of 3D Reconstruction of Rock Deformation in Tunnels based on a Deep Convolutional Neural Network
编号:904 访问权限:仅限参会人 更新:2021-12-03 10:31:55 浏览:92次 张贴报告

报告开始:2021年12月17日 10:22(Asia/Shanghai)

报告时间:1min

所在会场:[P1] Poster2020 [P1T2] Track 2 Transportation Infrastructure Engineering

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摘要
This paper presents a high-precision 3D method for non-contact measurement of the deformation of rock surrounding a tunnel. The method uses a 2D high-resolution photograph taken with a monocular camera and a 3D reconstruction technique to obtain a 3D image of rocks surrounding a tunnel. First, we analyze the basic principles of 3D-image-reconstruction technology. Next, we discuss methods for calculating the 3D shape of the known and unknown 2D key points and conclude that the use of deep convolutional neural network is an effective method and a new idea. Finally, we present findings that demonstrate that the 3D reconstruction method proposed in this paper can be used to obtain tri-dimensional models and deformation variables for the rock surrounding a tunnel that are superior to results obtained using traditional methods. The method is simple, provides good visibility, has a high degree of accuracy, and does not affect the physical structure of the tunnel.
关键词
CICTP
报告人
Xue Li
Guangzhou Metro Design & Research Institute Co., Ltd.

稿件作者
Xue Li Guangzhou Metro Design & Research Institute Co., Ltd.
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重要日期
  • 会议日期

    12月17日

    2021

    12月20日

    2021

  • 12月16日 2021

    报告提交截止日期

  • 12月24日 2021

    注册截止日期

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Chinese Overseas Transportation Association
Chang'an University
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