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Three-Dimensional Reconstruction and Monitoring of Large-Scale Structures via Real-Time Multi-vision System

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Artificial Intelligence Algorithms and Applications (ISICA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1205))

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Abstract

A four-ocular vision system is proposed for the three-dimensional (3D) reconstruction of large-scale concrete-filled steel tube (CFST) materials under complex testing conditions. These measurements are vitally important for evaluating the seismic performance and 3D deformation of large-scale specimens. A four-ocular vision system is constructed to sample the large-scale CFST, then point cloud acquisition, filtering, and stitching algorithms are applied to obtain 3D point cloud of the specimen surface. Novel point cloud correction algorithms based on geometric features and deep learning are proposed to correct the coordinates of the stitched point cloud. The proposed algorithms center on the stitching error of the multi-view point cloud and the geometric and spatial characteristics of the targets for error compensation, which makes them highly adaptive and efficient. A high-accuracy multi-view 3D model for the purposes of real-time complex surface monitoring can be obtained via this method. Performance indicators of the two algorithms were evaluated on actual tasks. The cross-section diameters at specific heights in the reconstructed models were calculated and compared against laser range finder data to test the performance of the proposed method. A visual tracking test on a CFST under cyclic loading shows that the reconstructed output well reflects the complex 3D surface after point cloud correction and meets the requirements for dynamic monitoring. The proposed method is applicable to complex environments featuring dynamic movement, mechanical vibration, and continuously changing features.

Y. Tang and M. Chen—Authors contributed equally to this work.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (51578162), the Key-area Research and Development Program of Guangdong Province (2019B020223003), and the Scientific and Technological Research Project of Guangdong Province (2016B090912005).

Author information

Authors and Affiliations

  1. College of Urban and Rural Construction, Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, China

    Yunchao Tang

  2. Key Laboratory of Key Technology on Agricultural Machine and Equipment, College of Engineering, South China Agricultural University, Guangzhou, 510642, China

    Mingyou Chen, Kuangyu Huang, Fengyun Wu, Xiangjun Zou & Yuxin He

  3. Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education and School of Civil Engineering, Harbin Institute of Technology, Harbin, 150001, China

    Xiangguo Wu

  4. Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin, 150090, China

    Yunchao Tang, Mingyou Chen, Xiangguo Wu, Kuangyu Huang, Fengyun Wu, Xiangjun Zou & Yuxin He

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  1. Yunchao Tang
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Correspondence to Yunchao Tang .

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Editors and Affiliations

  1. South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. Jiangxi University of Science and Technology, Ganzhou, China

    Wei Li

  3. South China Agricultural University, Guangzhou, China

    Hui Wang

  4. The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

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Tang, Y. et al. (2020). Three-Dimensional Reconstruction and Monitoring of Large-Scale Structures via Real-Time Multi-vision System. In: Li, K., Li, W., Wang, H., Liu, Y. (eds) Artificial Intelligence Algorithms and Applications. ISICA 2019. Communications in Computer and Information Science, vol 1205. Springer, Singapore. https://doi.org/10.1007/978-981-15-5577-0_35

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  • DOI: https://doi.org/10.1007/978-981-15-5577-0_35

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