Abstract

Fringe projection profilometry is a high-precision method used to measure the 3D shape of an object by projecting sinusoidal fringes onto an object. However, fringes projected onto a metallic or shiny object are distorted nonlinearly, which causes significant measurement errors. A high-precision measurement method for shiny objects that employs computer graphics (CG) and deep learning is proposed. We trained a deep neural network by projecting fringes on a shiny object in CG space. Our results show that the method can reduce the nonlinear fringe distortion caused by gloss in real space.

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