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.
In a conventional fringe projection profilometry (FPP) consisted of a camera and a projector, just one-sided 3D data of the tested object can be obtained by a single-shot measurement. Therefore, tools such as turntables are commonly used to obtain 360-degree 3D point cloud data of objects. However, this method requires multiple measurements and point cloud registration, which is time consuming and laborious. With the help of two planar mirrors, this paper proposes an improved system that captures fringe images from three different perspectives including one real camera and two virtual cameras. The information of the planar mirrors (i.e., the mirror calibration) is achieved by artificially attaching the featured pattern to the surface of the mirrors. Using the calibration parameters of the planar mirrors, the 3D point cloud data obtained by the virtual cameras can be converted into the real coordinate system, thereby reconstructing the full-surface 3D point cloud data with relative roughness. Finally, an improved ICP algorithm is introduced to obtain high-precision 360-degree point cloud data. The experimental results demonstrate that with the help of the mirrors, our system can obtain high-quality full-surface 360-degree profile results of the measured object at high speed.