Dynamic three-dimensional measurement using convolution neural network and binocular structured light system

Abstract

In this paper, a dynamic three-dimensional measurement method based on convolution neural network and binocular structured light system is proposed. We propose a convolution neural network to extract the real and imaginary terms of the first-order spectrum of a single frame fringe pattern. In our learning model, the loss function is established with output consistency, phase consistency and feature consistency as the joint constraints. And the dataset is built with actual deformed patterns of different scenes and frequencies. Furthermore, a dual frequency stereo phase unwrapping algorithm based on virtual plane is designed. Combined with the network, the absolute phase can be obtained by only two fringe projections in the measurement range, enabling the dynamic three-dimensional reconstruction of discontinuous or multiple isolated objects. The experimental results show the proposed network can significantly improve the accuracy of phase retrieve by 20 times compared to Fourier Transform Profilometry and the measurement error of the measurement system proposed in this paper for calibration sphere is less than 0.04mm. Furthermore, the measurement results of the dynamic process of palm unfolding verify the feasibility and the effectiveness of the proposed method.