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

Abstract

Deflectometry is a three-dimensional optical technique based on 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, 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 9 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 openedloops 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.

Time-Distributed Framework for 3D Reconstruction Integrating Fringe Projection with Deep Learning

“…Among various structured light techniques, fringe projection profilometry (FPP) is the most widely used technique in combination with the deep learning method for 3D reconstruction [ 38 , 39 , 40 , 41 , 42 ]. The integrated approaches can be broadly categorized into fringe-to-depth and fringe-to-phase techniques.…”