Compared to discrete-return and full-waveform LiDARs, the photon-counting LiDAR can provide more dense sampling, higher resolution, and better penetrability along the laser ranging. However, the point clouds obtained by the photon-counting LiDAR contain more background and interference noises. Therefore, it is necessary to adopt a suitable noise filtering method to accurately identify the effective photon signal on the target. At present, the main filtering methods include histogram statistics (HS), local distance statistics (LDS), and density-based spatial clustering of applications with noise (DBSCAN). To evaluate the performances of these methods on the mountainous region and the waters, the multiple altimeter beam experimental LiDAR is used for comparing and analyzing the three methods. The results show that the three methods can accurately extract effective photon point clouds. Among them, the HS method is suitable for flat terrains and water areas. LDS and DBSCAN are better suited for undulating terrains and mountainous areas, and DBSCAN achieves the best performance in mountainous areas. The results are quantitatively compared in terms of precision, recall, and F1-score. HS, LDS, and DBSCAN achieve a precision of 0.9342, 0.9524, and 0.9669, respectively, in the mountainous areas and 0.9981, 0.9492, and 0.9349, respectively, in the water areas.