Digital-twin-driven unambiguous structured light 3D imaging with physics-aware learning

Temporal phase unwrapping (TPU) plays a pivotal role in resolving phase ambiguities in fringe projection profilometry (FPP) caused by surface discontinuities or spatially isolated features. Although recent AI-based TPU methods significantly outperform traditional algorithms in processing noisy wrapped phases, they often depend on large-scale manually collected real-world datasets, which are time-consuming and labor-intensive. Moreover, these methods typically assume that training and testing data follow the same distribution, leading to dramatic accuracy degradation when applied to fringe images from unseen measurement systems. To overcome these limitations, we propose a digital-twin-driven, physics-aware framework for unambiguous structured-light 3D imaging. This framework leverages digital twin technology to generate vast amounts of realistic synthetic fringe images for training, while incorporating Fourier-domain consistency constraints and TPU physical models as priors. It establishes a generalized solution that supports multi-frequency (MF), multi-wavelength (MW), and number-theoretic (NT) TPU approaches. Experimental results show that the proposed network demonstrates exceptional generalization capabilities across unseen measurement systems. It achieves over 94% phase unwrapping accuracy for high-frequency fringes where conventional networks fail, performing comparably to models trained on real-world data. This research provides a promising pathway toward low-cost, high-precision, and highly generalizable intelligent optical metrology systems.