# Unsharp Mask Guided Filtering for Acoustic Point Cloud of Water-Conveyance Tunnel
## 2 Background and Related Work  
### Acoustic Point-Cloud Filtering Method  
Due to the complexity of underwater environments, the research on the filtering methods of acoustic point clouds, especially the acoustic point-cloud data of water-conveyance tunnels, is limited. [PERSON] et al. [17] proposed a multi-beam point-cloud filtering algorithm that considers the features of underwater terrain. This method is based on the Random Sample Consensus (RANSAC) ideal to fit the local plane, and the coplanar vector feature is used to remove outliers. However, it does not consider that outlier surfaces may be actual obstacles. [PERSON] et al. [18] used the intensity information and elevation information of points to filter the underwater acoustic point cloud. First, a rough classification was carried out according to the intensity information; then, accurate classification and filtering were realized using the intensity information and the elevation information of the seed point and its surrounding points. However, its filtering effect depends entirely on the accuracy of the seed point. [PERSON] et al. [19] studied the combined filtering algorithm of a multi-beam underwater acoustic point cloud. They divided the noise into large-scale and small-scale noise, and then used radius filtering and bilateral filtering, respectively, to achieve point-cloud smoothing. However, this method has the problem of the mistaken deletion of complex terrain. [PERSON] et al. [20] proposed a filtering method that fuses acoustic properties and intensity. They used the filtering method in the process of forming the acoustic point cloud and achieved good results. However, it is not suitable for the filtering of already formed acoustic point-cloud models. Therefore, these studies are not suitable for denoising the acoustic point clouds of water-conveyance tunnels.  
By contrast, several studies have been conducted on filtering methods for ground-laser point clouds. Bilateral filtering, originally introduced by [PERSON] et al. [21], is a robust edge-preserving filtering method, which has been applied to 3D mesh filtering [22; 23; 24]. However, these methods need to form mesh models, whose generation process inevitably suffers from noise [25]. Moreover, these methods are always imperfect at retaining the detailed features of point-based data models [26]. In order to overcome this problem, [PERSON] et al. [16] proposed an effective point-cloud denoising method by considering the position information of the point. However, in order to reduce the calculation cost, they ignore the fact that the point may be included in different neighborhoods when calculating the conversion coefficient. Therefore, the filtering effect in the acoustic point-cloud model is not ideal and the edge features of the point-cloud model are not well maintained in the smoothing process. [PERSON] et al. [15] proposed an approach that relies on the idea of implicitly defining a surface for a given set of points. The main idea is to define a projection procedure that projects any point near the point set onto a surface. Next, the MLS (moving least-squares) surface is defined as the set of points projecting onto itself. This method achieves a good smoothing effect, but it smooths the edge features of the point set because of the multiple projection process. Therefore, it is not suitable for surface-feature reconstruction based on the tunnel point cloud.  
Currently, most point-cloud filtering methods are based on laser-point cloud data. Owing to the particularity and complexity of the water conveyance tunnel project, the research on acoustic point-cloud filtering methods is limited. Therefore, the acoustic point-cloud filtering methods of water-conveyance tunnels have important practical research value.