Advanced Imaging, Volume. 1, Issue 2, 021004(2024)

Real-time 3D imaging based on ROI fringe projection and a lightweight phase-estimation network

Yueyang Li, Junfei Shen, Zhoujie Wu, Yajun Wang, and Qican Zhang*
Author Affiliations
  • College of Electronics and Information Engineering, Sichuan University, Chengdu, China
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    References(43)

    [8] P. Wissmann, R. Schmitt, F. Forster. Fast and accurate 3D scanning using coded phase shifting and high speed pattern projection. International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission, 108(2011).

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    [38] A. Paszke et al. PyTorch: an imperative style, high-performance deep learning library. Proceedings of the 33rd International Conference on Neural Information Processing Systems, 8026(2019).

    [39] V. Nair, G. E. Hinton. Rectified linear units improve restricted Boltzmann machines. Proceedings of the 27th International Conference on Machine Learning (ICML-10), 807(2010).

    [41] A. Paszke et al. ENet: a deep neural network architecture for real-time semantic segmentation(2016).

    [43] I. Loshchilov, F. Hutter. Decoupled weight decay regularization(2017).

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    Yueyang Li, Junfei Shen, Zhoujie Wu, Yajun Wang, Qican Zhang, "Real-time 3D imaging based on ROI fringe projection and a lightweight phase-estimation network," Adv. Imaging 1, 021004 (2024)

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    Paper Information

    Category: Research Article

    Received: Jun. 11, 2024

    Accepted: Sep. 2, 2024

    Published Online: Sep. 25, 2024

    The Author Email: Qican Zhang (zqc@scu.edu.cn)

    DOI:10.3788/AI.2024.10008

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