Zhong Chuqian, Gao Zhan, Wang Xu, Shao Shuangyun, Gao Chenjia
Key Laboratory of Luminescence and Optical Information of Ministry of Education, Beijing Jiaotong University, Beijing 100044, China.
Sensors (Basel). 2019 Jul 23;19(14):3229. doi: 10.3390/s19143229.
The three-dimensional measurement of structured light is commonly used and has widespread applications in many industries. In this study, machine learning is used for structured light 3D measurement to recover the phase distribution of the measured object by employing two machine learning models. Without phase shift, the measurement operational complexity and computation time decline renders real-time measurement possible. Finally, a grating-based structured light measurement system is constructed, and machine learning is used to recover the phase. The calculated phase of distribution is wrapped in only one dimension and not in two dimensions, as in other methods. The measurement error is observed to be under 1%.
结构光的三维测量被广泛应用，在许多行业中有着广泛的用途。在本研究中，机器学习被用于结构光三维测量，通过使用两个机器学习模型来恢复被测物体的相位分布。无需相移，测量操作的复杂性和计算时间的减少使得实时测量成为可能。最后，构建了构建了一个基于光栅的结构光测量系统，并使用机器学习来恢复相位。与其他方法不同，计算出的相位分布仅在一个维度上被包裹，而不是在两个维度上。观察到测量误差在1%以内。
