Single-shot fringe projection profilometry based on deep learning and computer graphics.

Abstract

Multiple works have applied deep learning to fringe projection profilometry (FPP) in recent years. However, to obtain a large amount of data from actual systems for training is still a tricky problem, and moreover, the network design and optimization is still worth exploring. In this paper, we introduce graphic software to build virtual FPP systems in order to generate the desired datasets conveniently and simply. The way of constructing a virtual FPP system is described in detail firstly, and then some key factors to set the virtual FPP system much closer to reality are analyzed. With the aim of accurately estimating the depth image from only one fringe image, we also design a new loss function to enhance the overall quality and detailed information is restored. And two representative networks, U-Net and pix2pix, are compared in multiple aspects. The real experiments prove the good accuracy and generalization of the network trained by the diverse data from our virtual systems and the designed loss, providing a good guidance for real applications of deep learning methods.