Complex object 3D measurement based on phase-shifting and a neural network

Abstract

An accurate phase-height mapping algorithm based on phase-shifting and a neural network is proposed to improve the performance of the structured light system with digital fringe projection. As phase-height mapping is nonlinear, it is difficult to find the best camera model for the system. In order to achieve high accuracy, a trained three-layer back propagation neural network is employed to obtain the complicated transformation. The phase error caused by the non-sinusoidal attribute of the fringe image is analyzed. During the phase calculation process, a pre-calibrated phase error look-up-table is used to reduce the phase error. The detailed procedures of the sample data collection are described. By training the network, the relationship between the image coordinates and the 3D coordinates of the object can be obtained. Experimental results demonstrate that the proposed method is not sensitive to the non-sinusoidal attribute of the fringe image and it can recover complex free-form objects with high accuracy.