Learning-based absolute 3D shape measurement based on single fringe phase retrieval and speckle correlation

In this work, we propose a learning-based absolute 3D shape measurement based on single fringe phase retrieval and speckle correlation. Our method combines the advantages of Fourier transform profilometry (FTP) techniques for high-resolution phase retrieval and speckle correlation approaches for robust unambiguous depth measurement. The proposed deep learning framework comprises two paths: one is a U-net-structured network, which is used to extract the wrapped phase maps from a single fringe pattern with high accuracy (but with depth ambiguities). The other stereo matching network produces the initial absolute (but with low resolution) disparity map from an additional speckle pattern. The initial disparity map is refined by exploiting the wrapped phase maps as an additional constraint, and finally, a high-accuracy high-resolution disparity map for absolute 3D measurement can be obtained. Experimental results demonstrated that the proposed deep-learning-based method could realize high-precision absolute 3D measurement for measuring objects with complex surfaces.