Passive binary defocusing for large depth 3D measurement based on deep learning

Phase-shifting profilometry (PSP) based on the binary defocusing technique has been widely used due to its highspeed capability. However, the required adjustment in projector defocus by traditional method is inaccurate, inflexible, and associated with fringe pitch. Instead of manual defocusing adjustment, a passive defocus of the binary patterns based on deep learning is proposed in this paper. Learning the corresponding binary patterns with a specifically designed convolutional neural network, high-quality three-step sinusoidal patterns can be generated. Experimental results demonstrate that the proposed method could reduce phase error by 80%-90% for different fringe pitches without projector defocus and outperform the traditional method by providing more accurate and robust results within a large measuring depth. (C) 2021 Optical Society of America

Automated summary

In this paper, a passive defocusing method based on deep learning is proposed to reduce phase error and generate high-quality three-step sinusoidal patterns. Experimental results show that this method outperforms the traditional method by providing more accurate and robust results within a large measuring depth.