Phase Completion for Fringe Projection Profiler Based on Neural Networks

Abstract

Fringe projection profiler (FPP) measures the geometry of the target surface by projecting the pre-modulated stripe map onto the surface, and then capture the phase map with a camera. However, the inaccurate exposure or the characteristics of the surface reflectance may influence the imaging quality of the phase map, leaving some over-exposure and under-exposure regions. Addressing to this problem, this paper propose to apply a neural network to complete the phase map. Firstly, we propose a synthetic dataset to simulate the phase map of the inaccurate exposure regions, based on a physical rendering model. After that, we implement a transformer neural network to complete the missing phase information. Experiments show that the proposed neural network can complete the missing information from its neighbouring information, and provide precise completion results.