Photometric Feature-Guided Phase Inpainting for High-Dynamic-Range Fringe Projection Profilometry of Non-Lambertian Surfaces

Fringe projection profilometry (FPP) is widely used for high-precision three-dimensional measurement, but its performance is severely degraded when measuring non-Lambertian surfaces with complex reflectance. In such cases, fringe saturation and low modulation lead to unreliable phase values and missing reconstructed data. To address this problem, this paper proposes a photometric feature-guided phase inpainting method for high-dynamic-range FPP. Within the proposed FPP with multi-illumination framework, fringe images are used for phase calculation, while multi-illumination images provide additional photometric cues for phase inpainting. A photometric feature-guided phase inpainting network is designed to extract photometric features from multi-illumination images and fuse them with the damaged phase map. The network is trained on a large-scale synthetic dataset and validated using a prototype system. In standard copper-sphere measurements, PF-PINet increases the average reliable reconstruction ratio from 81.85% to 84.31% while maintaining comparable reconstruction accuracy, indicating that the proposed method can improve measurement completeness for non-Lambertian surfaces.