Physics-informed neural network enabled high-fidelity compressive phase-shifting fringe projection profilometry
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Phase-shifting profilometry (PSP), as a high-precision and low-cost three-dimensional (3D) profile measurement technique, has been extensively applied in diverse fields. By introducing the compressive sensing paradigm, compressive phase-shifting ...
MorePhase-shifting profilometry (PSP), as a high-precision and low-cost three-dimensional (3D) profile measurement technique, has been extensively applied in diverse fields. By introducing the compressive sensing paradigm, compressive phase-shifting fringe projection profilometry (CPSFPP) provides an effective solution for high-speed dynamic object acquisition. Nevertheless, achieving high-fidelity dynamic profile reconstruction from compressed measurements remains a considerable challenge, owing to the inherent information loss in compressive sampling and limitations in computational reconstruction, especially under high compression ratios. To address this issue, we propose a physics-informed neural network (PINN)-based computational imaging framework for high-fidelity CPSFPP, named PINN-CPSFPP. This method integrates the physical model constraints with neural network learning to guarantee high-fidelity reconstruction even at high compression ratios. We perform numerical simulations to verify the reconstruction accuracy of PINN-CPSFPP under different compression ratios and experimentally validate the method by measuring translational, rotational, and deformed objects. The results demonstrate that the measurement speed is increased by nine times compared with conventional PSP. Benefiting from its robust 3D imaging performance, PINN-CPSFPP serves as a high-fidelity metrological tool for high-speed 3D scenarios and exhibits promising application prospects in a wide range of basic and applied disciplines.
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Bozhang Cheng, ... Shian Zhang
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DOI: https://doi.org/10.70401/lma.2026.0014 - June 05, 2026