With the development of the computer vision technology, research on recording and modeling the real world accurately and efficiently has become a key issue. Due to the limitation of hardware, the resolution of a point cloud is usually low, which cannot meet the applications. Therefore, it is necessary to study the super-resolution technology of point clouds. In this paper, we sort out the significance, progress, and evaluation methods of 3D point cloud super-resolution technology, introduce the classical super-resolution algorithm and the super-resolution algorithm based on machine learning, summarize the characteristics of the current methods, and point out the main problems and challenges in current point cloud data super-resolution technology. Finally, the future direction in point cloud super-resolution technology is proposed.