End-to-end single-shot composite fringe projection profilometry based on deep learning

Abstract

Using a single fringe image to complete the dynamic absolute 3D reconstruction has become a tremendous challenge and an eternal pursuit for researchers. In fringe projection profilometry (FPP), although many methods can achieve high-precision 3D reconstruction from simple system architecture via appropriate encoding ways, they usually cannot retrieve the absolute 3D information of objects with complex surfaces through only a single fringe pattern. In this work, we develop a single-frame composite fringe encoding approach and use a deep convolutional neural network to retrieve the absolute phase of the object from this composite pattern end to- end. The proposed method can directly obtain spectrum-aliasing-free phase information and robust phase unwrapping from single-frame compound input through extensive data learning. Experiments have demonstrated that the proposed deep-learning-based approach can achieve absolute phase retrieval using a single image.