Scene understanding is an important research content in information science. Compared with the two-dimensional (2D) data, the three-dimensional (3D) data has many advantages. At present, there are many ways to acquire the point clouds, and meanwhile the point clouds with different acquisition methods have different characteristics. In addition, there lacks a complete and systematic research review on the key techniques for 3D scenes understanding. Thus, the different methods for point cloud acquisition are summarized, and the different point cloud data and related databases are compared and analyzed as well. Based on the current research progress of 3D scene understanding, the techniques for point cloud filtering, feature extraction, point cloud segmentation, and point cloud semantic segmentation in 3D scene understanding are compared and summarized. By the summary of the domestic and foreign literatures published in recent years, the problems occurred in the key technologies for 3D scene understanding are condensed, and the development trend of the 3D scene understanding problems is prospected. The 3D scene understanding based on point clouds is widely used in many fields due to its richness of data. However, as for the scene understanding effect of 3D point clouds, especially the scene understanding of laser point clouds with color information, there are still many contents needed to be investigated in depth.