用于单次绝对3D形状测量的支持深度学习的几何约束和相位展开
Deep-learning-enabled geometric constraints and phase unwrapping for single-shot absolute 3D shape measurement
摘要：
条纹投影轮廓术（Fringe projection profilometry，FPP）是一种非常流行的三维面形测量技术，在智能制造、缺陷检测等重要应用领域得到了越来越广泛的应用。在FPP中，如何有效地恢复绝对相位一直是一个很大的挑战。基于几何约束的立体相位展开（SPU）技术可以消除相位模糊，而不需要投影任何额外的条纹图，从而最大限度地提高了绝对相位的恢复效率。受最近深度学习技术在相位分析中的成功启发，我们证明了深度学习可以成为一种有效的工具，将相位检索，几何约束和相位展开步骤有机地统一到一个全面的框架中。在大量训练数据集的驱动下，神经网络可以逐渐“学习”如何将一个高频条纹图案转换为“物理上有意义的”和“最有可能的”绝对相位，而不是像传统方法那样“一步一步”。基于适当训练的框架，仅基于单帧投影就可以实现高质量的相位恢复和鲁棒的相位模糊消除。实验结果表明，与传统的SPU方法相比，该方法能够在更大的测量体积内，以较少的摄像机视角，更高效、稳定地对密集条纹图像进行相位展开。所提出的方法的局限性进行了讨论。我们相信，所提出的方法代表了一个重要的一步，在高速，高精度，无运动伪影的绝对三维形状测量复杂的物体从一个单一的条纹图案。
英文摘要：
Fringe projection profilometry (FPP) is one of the most popular three-dimensional (3D) shape measurement techniques, and has becoming more prevalently adopted in intelligent manufacturing, defect detection and some other important applications. In FPP, how to efficiently recover 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 fringe patterns, which maximizes the efficiency of the retrieval of absolute phase. Inspired by the recent success of deep learning technologies for phase analysis, we demonstrate that deep learning can be an effective tool that organically unifies the phase retrieval, geometric constraints, and phase unwrapping steps into a comprehensive framework. Driven by extensive training dataset, the neutral network can gradually "learn" how to transfer one high-frequency fringe pattern into the "physically meaningful", and "most likely" absolute phase, instead of "step by step" as in convention approaches. Based on the properly trained framework, high-quality phase retrieval and robust phase ambiguity removal can be achieved based on only 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 the proposed approach represents an important step forward in high-speed, high-accuracy, motion-artifacts-free absolute 3D shape measurement for complicated object from a single fringe pattern.
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