# Unsharp Mask Guided Filtering for Acoustic Point Cloud of Water-Conveyance Tunnel
## 1 Introduction  
Water-conveyance tunnels are the main structures of water-diversion projects. When water-diversion projects enter the operational period, special attention must be paid to the health status of water-conveyance tunnels. The regular inspection of water-conveyance tunnels is an important means of ensuring their safe operation and health management. In recent years, in terms of the problem of tunnel-damage detection, researchers have proposed many methods and technologies (e.g., side-scan sonar, multi-beam sonar, and synthetic-aperture sonar) [1, 2, 3, 4]. The detection method of tunnel damage based on the principle of pulse reflection forms an acoustic point-cloud model by sampling the reflected echo, and then detects and identifies the damage through acoustic point-cloud data [5]. However, affected by the noise of tunnel environments and inspection systems, as well as the noise of carrier equipment (robot carriers and submersibles, etc.), the raw acoustic point-cloud models obtained from these methods inevitably suffer from noise, which makes it difficult to obtain useful information and increases the fuzziness and randomness of information features. In this case, the detection methods that rely on information features produce erroneous results very easily. Therefore, a filtering operation must be performed on the raw acoustic point-cloud model of the tunnel before further processing (e.g., object recognition [6, 7] or 3D reconstruction [8]).  
Inspired by the excellent results of the guided image-filtering algorithm introduced by [PERSON] et al. [9], the classical sharpness enhancement technique of unsharp masking [10, 11, 12] and the edge-aware weighted guided image-filtering algorithm [13], this study extends theseeffective methods to the point-cloud model of tunnel and proposes a new filtering method, called unsharp-mask-guided filtering for acoustic point-cloud of water-conveyance tunnel, by considering the position of the point instead of the pixel value. The experimental results show that our method can outperform several competing methods (e.g., bilateral filter [14], moving least-squares filter [15], and guided 3D point-cloud filter [16]), both on the acoustic point-cloud tunnel model and the simulated point-cloud model.  
The contributions of our work can be summarized as follows: (1) the idea of guided filtering and the unsharp masking technique are fused to design a novel filtering approach for an acoustic point cloud for water-conveyance tunnels; (2) the idea of edge-aware weighting is used to retain the features while smoothing some of the edges of the point cloud; (3) a comprehensively experimental evaluation between our method and several competing denoising methods is conducted on the tunnel point-cloud model and simulated point-cloud model, respectively.  
The remainder of this paper is structured as follows. A brief overview of the background and related work is given in Section 2. Section 3 describes the principle and implementation process of our proposed algorithm in detail. The experimental process and experimental results on different point-cloud models are demonstrated in Section 4. Conclusions and discussions are presented in Section 5.