A deep learning-based approach to solve the height-slope ambiguity in phase measuring deflectometry

Abstract

Phase measuring deflectometry (PMD) shows excellent performance in measuring the shape of specular surfaces. However, the existing height-slope ambiguity affects the measurement of PMD. In this paper, we propose a deep learning-based method to solve the ambiguity and reconstruct the specular surface. A neural network was built to reconstruct the surface. A set of simulation surfaces were generated using two-dimensional Legendre polynomials to train and test the network. A spherical-shaped surface was reconstructed in a simulation to evaluate the validity of the proposed method and compare our method to the state-of-the-art method. Experimental results demonstrate a 35.88% improvement in this method’s accuracy. Our method provides a great improvement in measurement accuracy.

High-precision binocular fringe reflectometry based on normal vector consistency

“…However, the traditional phase deflection technique, which has been subject to ongoing research and discoveries, introduces ambiguity in the mathematical model. Subsequent research aims to mitigate this ambiguity or propose alternative measurement models [7][8][9]. Nevertheless, these models may result in unreliable positioning accuracy in hardware system or require higher calibration precision.…”