Fringe projection profilometry (FPP) has become a more prevalently adopted technique in intelligent manufacturing, defect detection, and some other important applications. In FPP, efficiently recovering the absolute phase has always been a great challenge. The stereo phase unwrapping (SPU) technologies based on geometric constraints can eliminate phase ambiguity without projecting any additional patterns, which maximizes the efficiency of the retrieval of the absolute phase. Inspired by recent successes of deep learning for phase analysis, we demonstrate that deep learning can be an effective tool that organically unifies phase retrieval, geometric constraints, and phase unwrapping into a comprehensive framework. Driven by extensive training datasets, the neural network can gradually “learn” to transfer one high-frequency fringe pattern into the “physically meaningful” and “most likely” absolute phase, instead of “step by step” as in conventional approaches. Based on the properly trained framework, high-quality phase retrieval and robust phase ambiguity removal can be achieved only on a single-frame projection. Experimental results demonstrate that compared with traditional SPU, our method can more efficiently and stably unwrap the phase of dense fringe images in a larger measurement volume with fewer camera views. Limitations about the proposed approach are also discussed. We believe that the proposed approach represents an important step forward in high-speed, high-accuracy, motion-artifacts-free absolute 3D shape measurement for complicated objects from a single fringe pattern.

中文翻译：

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启用深度学习的几何约束和相位展开，可实现单次绝对3D形状测量

条纹投影轮廓仪（FPP）已成为智能制造，缺陷检测和其他一些重要应用中越来越普遍的技术。在FPP中，有效地恢复绝对相位一直是一个巨大的挑战。基于几何约束的立体声相位展开（SPU）技术可以消除相位模糊性，而无需投影任何其他模式，从而最大程度地提高了绝对相位的检索效率。受到深度学习在相位分析方面的最新成功的启发，我们证明了深度学习可以是一种有效的工具，可以将相位检索，几何约束和相位展开有机地统一到一个综合框架中。在广泛的培训数据集的推动下，神经网络可以逐步“学习”以将一种高频条纹模式转换为“具有物理意义”和“最可能的”绝对相位，而不是像常规方法那样“逐步”进行转换。基于训练有素的框架，仅在单帧投影上就可以实现高质量的相位检索和可靠的相位模糊度消除。实验结果表明，与传统的SPU相比，我们的方法可以在更有效的测量范围内以较少的摄像机视角更有效地解开密集条纹图像的相位。还讨论了所提出方法的局限性。我们相信，所提出的方法代表了从单个条纹图案对复杂对象进行高速，高精度，无运动伪像的绝对3D形状测量的重要一步。