Profile measurement of non-Lambertian surfaces by integrating fringe projection profilometry with near-field photometric stereo

Abstract

Fringe projection profilometry (FPP) performs poorly when measuring non-Lambertian surfaces, since the measured points are missing in specular regions. The existing solutions need to make a trade-off between precision and efficiency, because no extra information about specular regions is available. To tackle the specular reflection problem of FPP, this paper proposes a multi-sensor measurement method by integrating FPP with near-field photometric stereo (NFPS). Firstly, the point clouds in non-specular regions are measured by FPP. Then, a deep neural network is applied to estimate the normal map of the surface by NFPS. Finally, the intact point clouds are reconstructed by minimizing the depth gradient error calculated from the normal map. The proposed method can achieve high-precision measurement without any human intervention. In the experiment of measuring an aluminum alloy free-form surface, the deviation of the profile error between the proposed method and a coordinate measuring machine is only 6.9 µm.