## 5 Discussion  
At present, Gaussian outlier removal (GOR), statistical outlier removal (SOR), and radius outlier removal (ROR) are commonly used for point cloud denoising [43]. GOR pulls outliers back into dense places, correcting the noise by as much as possible, rather than removing the noise points. ROR uses the K-D tree to find a point within a specified radius and removes the point if there are fewer than the specified number of points in the radius. The SOR method calculates the average distance between each point and its nearest \(\mathbf{k}\) points. The average distance is assumed to conform with the Gaussian distribution. Given the mean and variance, points for which the average distance is outside the standard range are defined as outliers and removed from the data. In this study, these methods are used to denoise the whole point cloud in the dry beach area. A profile is divided respectively and compared with the denoising method proposed in this paper, as shown in Figure 18.  
Figure 17: Section of dry beach point cloud model. Figure 18: Point cloud denoising. (**a**) Original point cloud. (**b**) Gaussian outlier removal. (**c**) Statistical outlier removal. (**d**) Radius outlier removal. (**e**) Outlier removed using the proposed method. According to Section 3.3.2, four criteria were calculated to evaluate the effect of denoising, and the results are shown in Table 2. The ODP, recall, and accuracy of the denoising method in this paper are close to those of the GOR method and far better than the SOR and ROR methods. Moreover, the RMSE of the denoising methods in this paper is smaller than the other three methods. This indicates a better denoising effect. However, the denoising method in this paper is only for dry beach point clouds, which is difficult to apply to point cloud models of other scenes. In the field of dry beach measurements, traditional measurement methods, such as laser measurement and slope estimation, measure the slope of the dry beach based on the geometric relationship and the length of the dry beach by combining the height difference between the beach top and the waterline. These methods are usually used to measure the dry beach parameters of a certain section but cannot cover the entire dry beach area. The dry beach measurement method based on image processing uses image segmentation and edge-detection operators to identify water lines. Then, the pixel scale is converted to the real scale to measure the length of the dry beach, and the slope of the dry beach is obtained by combining the height difference. However, the main limitation of this method is the lack of three-dimensional spatial information, which cannot describe the surface morphology and slope variations of the dry beach. Compared with the above methods, this study uses 3D point cloud data to measure the length and slope of the dry beach. Three-dimensional point cloud data can provide more abundant topographic relief information and truly describe the three-dimensional surface morphology of the dry beach. This method not only allows for a measurement of the slope and length at any given point across the entire dry beach area but also meets the relevant regulations of tailings pond monitoring, offering distinct advantages in the monitoring of complex terrain and large-scale tailings ponds. In this study, 3D point cloud data were used to assess the dry beach of the tailings pond, and the results were satisfactory. However, there are still some limitations. The 3D reconstruction process based on UAV imagery is inherently time-consuming, with computational demands increasing significantly for larger tailings pond areas. The timeliness of monitoring results is reduced when a large number of high-resolution images are used for reconstruction. If the range of the tailings pond is large, a substantial number of images are typically acquired, resulting in extended processing times for 3D modeling, which makes it difficult to realize the real-time monitoring of dry beaches.