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 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.

Metasurfaces composed of subwavelength-scale artificial meta-atoms have emerged as a powerful platform for manipulating light. By enabling strong light-matter interactions within ultrathin planar geometries, metasurfaces have opened new avenues for highly integrated photonic devices. Lithium niobate (LiNbO₃) is considered one of the most promising multifunctional integrated photonic platforms due to its outstanding properties, such as a large second-order nonlinear susceptibility, a broad transparency window, and a strong electro-optic (EO) effect. In recent years, integrated photonic devices based on lithium-niobate-on-insulator platforms have experienced rapid development. This review summarizes recent advances in LiNbO₃ metasurfaces, providing a comprehensive overview of their demonstrated applications in nonlinear frequency conversion, wavefront and phase modulation, and dynamic EO modulation. By systematically introducing the interplay between the intrinsic material properties of LiNbO₃ and the structural design principles of metasurfaces, this review offers a coherent framework for understanding their nonlinear and active optical functionalities, and serves as a valuable reference for the design and implementation of nonlinear and actively tunable micro- and nano-photonic devices.