Single-shot 3D shape measurement with spatial frequency multiplexing using deep learning

Abstract

In this paper, we propose a single-shot 3D shape measurement with spatial frequency multiplexing using deep learning. Fourier transform profilometry (FTP) is highly suitable for dynamic 3D acquisition and can provide the phase map using a single fringe pattern. However, it suffers from the spectrum overlapping problem which limits its measurement quality and precludes the recovery of the fine details of complex surfaces. Furthermore, FTP adopts the arctangent function ranging between -π and Π for phase calculation, which results in phase ambiguities in the wrapped phase map with 2π phase jumps. Inspired by deep learning techniques, in this study, we use a deep neural network to extract the phase information of the object from one deformed fringe pattern. Meanwhile, we design a dual-frequency fringe pattern with spatial frequency multiplexing to eliminate the phase ambiguities. Therefore, an absolute phase map can be obtained without projecting any additional patterns. The experimental results demonstrate that the single-shot 3D measurement method based on deep learning techniques can effectively realize the absolute 3D measurement with one fringe image and improve the measurement accuracy compared with the traditional Fourier transform profilometry.