Real Time 3D Measurement with Dual-frequency Fringes by Deep Learning

Fringe projection technology is a commonly used technique in optical three-dimensional measurement. In high-speed motion scenarios, due to image noise and the effects of object motion, projecting more fringe patterns for high-precision phase unwrapping is a common method, which can significantly reduce the frame rate of three-dimensional reconstruction. Deep learning techniques have been employed for high-precision phase unwrapping, but typically these models have a large parameter and computation, making them difficult to integrate into real-time three-dimensional reconstruction systems. In this paper, we first employ the LUT technique for rapid computation of dual-frequency phases. Secondly, we design a deep learning model with a parameter size of only 276kb for high-precision phase unwrapping, and quickly embed it into a real-time three-dimensional reconstruction system through 8bit quantization without compromising accuracy. Furthermore, we utilize the calibration parameters of a real FPP system to establish a corresponding virtual FPP system for rapid generation of data required for model training. Lastly, we optimize the generation of point clouds by avoiding the computationally slow inverse matrix operation process. Experiments show that our model can achieve high-precision real-time three-dimensional reconstruction at a rate of 130 frames per second.