Single-shot measurement of object with high reflectivity surfaces base on deep learning

Abstract Fringe projection profilometry (FPP) technique is a widely used method in three-dimensional measurement and has broad applications in industrial measurement. This method typically requires projecting multiple phase-shifted fringe patterns to achieve high-precision imaging. However, in industrial contexts, the measured objects are often metallic components with high reflectivity, which would adversely affect the reconstruction quality of FPP. High Dynamic Range (HDR) technology can effectively address such issues; however, it requires an increased number of projected fringe patterns, which reduces measurement efficiency. This paper proposes a single-shot 3D measurement method that combines traditional HDR techniques with deep learning to measure objects with highly reflective surfaces. The method employs two network models: the Phase Recovery Network (PRNet) and the Coarse Phase Unwrapping Network (CPUNet). PRNet receives a single fringe image with saturated pixels and outputs the numerator and denominator terms for phase retrieval. CPUNet takes the wrapped phase data processed through an arctangent function from PRNet’s output and predicts the coarse unwrapped phase to assist in phase unwrapping, yielding high-precision phase results. Experiments show that this approach requires only one shiny fringe image as input and effectively restores phase errors caused by high reflectivity, achieving a high reconstruction accuracy for the complete 3D shape of the object.