# Unsharp Mask Guided Filtering for Acoustic Point Cloud of Water-Conveyance Tunnel
## 5 Conclusions and Discussion  
To address the safety-monitoring problem of a water-conveyance tunnel during the operation stage, we proposed a novel filtering method, called unsharp-mask-guided filtering for 3D point cloud, to reduce the impact of noise on the point-cloud model of the water-conveyance tunnel. The proposed method fuses the ideas of classical guided filtering and the unsharp masking technique and extends them to the 3D point-cloud model by considering the position of the point. In addition, an edge-aware weighting idea was also used to retain the edge features of the point-cloud model while smoothing the noise points.  
In the experiment, we examined the influence of two key parameters on the experimental results. We also carried out experiments on real-world 3D models of the tunnel and nine simulated 3D models to compare our algorithm with some state-of-the-art methods. The experimental results show that the proposed method can outperform several competing methods, both on the real-tunnel point-cloud model and simulated point-cloud models. The model provides greater accuracy for the further processing of point clouds. However, we did not consider the efficiency of our algorithm in the experiment. Because our method needs to search the neighborhood twice for each point and to judge the inclusion relationship between the neighborhoods of different points, it does not have an obvious advantage in efficiency. The running time of point-cloud models with different number of points is different. Taking the Stanford rabbit model as an example, the time cost of our algorithm on the hardware device based on this study is 7.2 s, while the time costs of bilateral filtering, MLS filtering, and guidance filtering are 4.5 s, 4.8 s, and 3.7 s, respectively. These time costs are all higher than those in the references because of the weak processing power of the computer equipment used in this study. Therefore, we need to optimize the calculation method to improve the efficiency of the proposed algorithm in future work.  
Conceptualization, J.W. and X.Z.; methodology, J.W.; software, J.W.; validation, [PERSON], X.Z. and Z.Z.; formal analysis, [PERSON]; investigation, [PERSON]; resources, X.Z.; data curation, J.W.; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R&D Program of China, grant number 2018 YFC0407101, and National Natural Science Foundation of China, grant number 61671202.  
Not applicable.  
The data presented in this study are available from the corresponding author upon request.  
The authors would like to thank all the reviewers for their valuable comments. We would like to thank Georgia Tech for their Large Geometric Models. We are grateful to Princeton ModelNet project for sharing the ModelNet datasets. We also thank the researchers at Princeton, Stanford, and TTIC for sharing the ShapeNet datasets.  
The authors declare no conflict of interest.