High-speed high dynamic range 3D shape measurement based on deep learning

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).