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Trusiak M, Kujawinska M. Deep learning enabled single-shot absolute phase recovery in high-speed composite fringe pattern profilometry of separated objects. Opto-Electron Adv 6, 230172 (2023). doi: 10.29026/oea.2023.230172
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News & Views Open Access
Deep learning enabled single-shot absolute phase recovery in high-speed composite fringe pattern profilometry of separated objects
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Abstract
A recent article in the Opto-Electronic Advances (OEA) journal from Prof. Qian Chen and Prof. Chao Zuo’s group introduced a new and efficient 3D imaging system that captures high-speed images using deep learning-enabled fringe projection profilometry (FPP). In this News & Views article, we explore potential avenues for future advancements, including expanding the measurement range through an extended number-theoretical approach, enhancing quality through the incorporation of horizontal fringes, and integrating data from other modalities to broaden the system's applications. -
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References
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Trusiak M, Kujawinska M. Deep learning enabled single-shot absolute phase recovery in high-speed composite fringe pattern profilometry of separated objects. Opto-Electron Adv 6, 230172 (2023). doi: 10.29026/oea.2023.230172
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Figure 1.
Back cover of Volume 5, Issue 5 of OEA in 2022.
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Figure 2.
Possible future extension of the proposed DCFPP technique: (a) the example triple-frequency projected vertical and cross fringe patterns and their spectra, (b) the hardware system and (c) the enhanced shape reconstruction using cross fringe pattern and two channel reconstructions for vertical and horizontal composite fringe patterns.
