DYnet++: A Deep Learning Based Single-Shot Phase-Measuring Deflectometry for the 3-D Measurement of Complex Free-Form Surfaces

Deflectometry is a three-dimensional optical technique based on the structured light projection for measuring specular free-form surfaces. Much research on single-shot deflectometry has been conducted to develop reliable measurement techniques, even for harsh industrial environments. But the measurement of complex surfaces with low reflectivity is still a big challenge in single-shot deflectometry since it is very tricky to obtain the phase from a poor quality single complex pattern. Inspired by recent applications of deep learning in the optical metrology field, in this article, we propose a novel single-shot deflectometry that utilizes deep learning to measure complex surfaces with low quality in just a single shot. A deformable mirror with nine actuators was developed to generate big data for various surface shapes to train the deep learning model. We developed a deep learning network model, DYnet++, to retrieve the phase from single composite patterns even where both closed and opened loops are presented. We verified the feasibility of the proposed deep learning based single-shot deflectometry by comparing measurement results with those from 16-step phase-shifting method.