  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 still worth exploring. In this paper, we introduce
computer graphics 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 close to the 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 quality of the overall and detailed information
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 data from our virtual systems and
the designed loss, implying the potential of our method for applications.
