High-precision dynamic three-dimensional shape measurement of specular surfaces based on deep learning

Abstract

In order to solve the difficulty of traditional phase measuring deflectometry (PMD) in considering precision and speed, an orthogonal encoding PMD method based on deep learning is presented in this paper. We demonstrate for, what we believe to be, the first time that deep learning techniques can be combined with dynamic-PMD and can be used to reconstruct high-precision 3D shapes of specular surfaces from single-frame distorted orthogonal fringe patterns, enabling high-quality dynamic measurement of specular objects. The experimental results prove that the phase and shape information measured by the proposed method has high accuracy, almost reaching the results obtained by the ten-step phase-shifting method. And the proposed method also has excellent performance in dynamic experiments, which is of great significance to the development of optical measurement and fabrication areas.