Self-Learning-Based Fringe Domain Conversion for 3D Surface Measurement of Translucent Objects at the Mesoscopic Scale

Three-dimensional measurement of translucent objects using structured light techniques remained fundamentally challenging due to severe degradation of fringe patterns caused by subsurface scattering, which inevitably introduced phase errors and compromised measurement accuracy. Although deep learning had emerged as a powerful tool for fringe analysis, its practical implementation was hindered by the impractical requirement for large-scale labeled datasets, particularly in scattering-dominant measurement scenarios. To overcome these limitations, we developed a self-learning-based fringe domain conversion method inspired by image style transfer principles, where degraded and ideal fringe patterns were treated as distinct domains for cyclic translation. The proposed framework employed dual generators and discriminators to establish cycle-consistency constraints while incorporating both numerical intensity-based and physical phase-derived optimization targets, effectively suppressing phase errors and improving fringe modulation without requiring paired training data. Experimental validation demonstrated superior performance in reconstructing high-fidelity 3D morphology of translucent objects, establishing this approach as a robust solution for precision metrology of complex scattering media.