Defocus projection three-dimensional measurement based on deep learning accurate phase acquisition

The digital fringe projection three-dimensional (3D) measurement technology can generate a sinusoidal fringe pattern for 3D measurement by defocusing a binary fringe pattern. It can achieve extremely high projection speed and has great potential in the field of high-speed 3D measurement. However, the binary fringe pattern inevitably contains higher-order harmonics, resulting in a phase error introduced into the calculated phase, thereby reducing the accuracy of high-speed 3D measurement. A 3D measurement method for defocused projection based on deep learning accurate phase acquisition was proposed. The image feature processing capability based on deep learning algorithm can remove the phase errors introduced by higher-order harmonics. An end-to-end deep convolutional neural network from noise phase to precise phase was constructed by this method and the phase error introduced by higher-order harmonics was reduced. Finally, high-speed and accurate 3D measurement could be achieved by this method. Firstly, the theoretical analysis proved the feasibility of the proposed method. Then, simulation and experiments were performed to further verify the effectiveness and accuracy of the proposed method. Compared with the existing high-speed 3D measurement methods, the proposed method can ensure measurement speed while ensuring measurement accuracy.