Deep learning technique has exhibited promising performance in achieving high-resolution (HR) images from their low-resolution (LR) images in the super-resolution (SR) field. However, most of the existing SR methods have two underlying problems. First, degraded datasets (i.e., bicubic downsampling) are usually used to train and evaluate the network model, which may lead to less effective in practical scenarios. Second, the 2-D-to-3-D SR technique is lacking. In this article, a real-world 2-D-to-3-D technique is developed to realize SR 3-D shape from 2-D fringe images in fringe projection profilometry (FPP). An FPP system consisting of one projector and a dual camera is applied to obtain the real-world dataset where paired LR-HR images on the same scene are captured. The 3-D geometrical constraints solved from the FPP system are employed to align the image pairs by pixel-to-pixel mapping so that a more accurate dataset can be obtained. In addition, a flexible multiple-to-two network structure is introduced to achieve an SR 3-D point cloud from multiple phase-shifting patterns. Experiments demonstrate the comparison between traditional degraded training and our training.