Abstract For many three-dimensional (3D) measurement techniques based on fringe projection profilometry (FPP), measuring the objects with a large variation range of surface reflectivity is always a very tricky problem due to the limited dynamic range of camera. Many high dynamic range (HDR) 3D measurement methods are developed for static scenes, which are fragile for dynamic objects. In this paper, we address the problem of phase information loss in HDR scenes, in order to enable 3D reconstruction from saturated or dark images by deep learning. By using a specifically designed convolutional neural network (CNN), we can accurately extract phase information in both the low signal-to-noise ratio (SNR) and saturation situations after proper training. Experimental results demonstrate the success of our network in 3D reconstruction for both static and dynamic HDR objects. Our method can improve the dynamic range of three-step phase-shifting by a factor of 4.8 without any additional projected images or hardware adjustment during measurement. And the final 3D measurement speed of our method is about 13.89 Hz (off-line).

中文翻译：

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基于深度学习的高速高动态范围3D形状测量

摘要 对于许多基于条纹投影轮廓法（FPP）的三维（3D）测量技术，由于相机的动态范围有限，测量表面反射率变化范围大的物体一直是一个非常棘手的问题。许多高动态范围 (HDR) 3D 测量方法是为静态场景开发的，这些方法对于动态对象来说是脆弱的。在本文中，我们解决了 HDR 场景中的相位信息丢失问题，以便通过深度学习从饱和或黑暗图像中进行 3D 重建。通过使用专门设计的卷积神经网络 (CNN)，我们可以在适当训练后在低信噪比 (SNR) 和饱和情况下准确提取相位信息。实验结果证明了我们的网络在静态和动态 HDR 对象的 3D 重建中的成功。我们的方法可以将三步相移的动态范围提高 4.8 倍，而无需在测量过程中进行任何额外的投影图像或硬件调整。我们方法的最终 3D 测量速度约为 13.89 Hz（离线）。