Infrared and Laser Engineering, Volume. 49, Issue 6, 20200023(2020)

Multi-stage deep learning based single-frame fringe projection 3D measurement method

Zhao Zhang... Bowen Han, Haotian Yu, Yi Zhang, Dongliang Zheng and Jing Han* |Show fewer author(s)
Author Affiliations
  • 南京理工大学 电子工程与光电技术学院,江苏 南京 210094
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    The application of deep learning has simplified the process of 3D measurement of digital fringe projection. In the process of fringe projection, phase calculation, phase unwrapping, and phase-depth mapping of traditional digital fringe projection 3D measurement technology, researchers have successfully demonstrated the feasibility of combining the first three stages and the entire process with deep neural networks. Based on deep learning, the Phase to Depth Network (PDNet) was proposed to achieve the map from absolute phase to depth. Combined with multi-stage deep learning based single-frame fringe projection 3D measurement method, the absolute phase and depth information of the object were obtained by deep learning in stages. The experimental results show that the PDNet can measure the depth information of the object comparatively accurately, and the application of deep learning is feasible in the phase-height mapping stage. And compared with the single-stage deep learning based single-frame fringe projection 3D measurement method that directly maps from the fringe image to the three-dimensional topography information, multi-stage deep learning based single-frame fringe projection 3D measurement method can significantly improve the measurement accuracy, which only require a single fringe input to obtain millimeter-level measurement accuracy, and it can adapt to 3D measurement of objects with complex surfaces.

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    Zhao Zhang, Bowen Han, Haotian Yu, Yi Zhang, Dongliang Zheng, Jing Han. Multi-stage deep learning based single-frame fringe projection 3D measurement method[J]. Infrared and Laser Engineering, 2020, 49(6): 20200023

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    Paper Information

    Category: Special issue-Optical 3D imaging and sensing

    Received: Mar. 19, 2020

    Accepted: --

    Published Online: Aug. 19, 2020

    The Author Email: Han Jing (eohj@njust.edu.cn)

    DOI:10.3788/IRLA20200023

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