深度学习精确相位获取的离焦投影三维测量

数字光栅投影三维测量技术，通过离焦投影二值光栅条纹，生成三维测量所需正弦光栅条纹，能够实现超高投影速度，在快速三维测量领域具有极大潜力。但是，二值光栅条纹不可避免地包含高次谐波，导致计算所得相位包含相位误差，进而降低了快速三维测量精度。提出了一种基于深度学习精确相位获取的离焦投影三维测量方法，通过构建含噪声相位到精确相位的端到端深度卷积神经网络，降低高次谐波引入的相位误差，进而实现快速精确三维测量。首先，以理论分析证明所提方法的可行性，并以仿真和实验进一步验证了所提方法的有效性和精确性。与现有快速三维测量方法相比，所提方法在保证测量速度的同时保证测量精度。

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.