Point cloud is a special data form for 3-D image, which is gradually becoming a research hotspot of 3-D image information processing. Point cloud segmentation plays an important role in point cloud processing and has a direct impact on the results of the algorithm. Point clouds segmentation algorithm that based on geometric features of 3-D images are simpler in structure, more stable in operation results, and easy to adjust, which occupy a major position in practical applications. In this work, the point clouds segmentation methods based on geometric features emerged in recent years were sorted out. According to the theoretical basis and application characteristics of each method, the algorithms were classified into three categories: Edge detection based, surface features based, and model fitting based methods. The characteristics, the main problems of different algorithms, and the main factors that affect the efficiency have been analyzed. Finally, the algorithms performance have been compared, and the future development trend is prospected.