Li Yueyang, Shen Junfei, Wu Zhoujie, Zhang Qican
Appl Opt. 2021 Aug 20;60(24):7243-7253. doi: 10.1364/AO.432085.
Phase-shifting profilometry (PSP) based on the binary defocusing technique has been widely used due to its high-speed 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.
基于二元散焦技术的相移轮廓术（PSP）因其高速能力而被广泛应用。然而，传统方法对投影仪散焦所需的调整不准确、不灵活，且与条纹间距相关。本文提出了一种基于深度学习的二元图案被动散焦方法，以替代手动散焦调整。通过专门设计的卷积神经网络学习相应的二元图案，可以生成高质量的三步正弦图案。实验结果表明，该方法在不进行投影仪散焦的情况下，对于不同的条纹间距可将相位误差降低80%-90%，并且在较大的测量深度范围内能提供更准确、更稳健的结果，优于传统方法。
