Accuracy analysis of fringe projection profilometry using raytracing algorithm and BP neural network

Fringe projection profilometry is widely used in manufacturing and the accuracy analysis is the key to promote this technology in engineering applications. Researches analyze influencing factors including gamma effect, intensity noise, defocus and methods are proposed to improve the measurement accuracy. However, an analytical study is difficult to perform and the surface shape of the measuring objects influence the fringe images which needs to be considered. In this paper, raytracing algorithm and back-propagation network are used to study the relationship between the surface shape and measurement accuracy. The fringe projection profilometry system is simulated in computer using the raytracing algorithm and the light transport coefficients are measured to improve the accuracy of the camera defocus simulation. The impact of surface shape on fringe images is analyzed, and the projection and observation angles are used as the input of the network. The truth value of the surface is known in the simulation model thus the error of the coordinate can be obtained after simulation measurement and used as the output of the network. Experiment show that, high correlation exists between the surface shape and the coordinate error.