Unsharp Mask Guided Filtering for Acoustic Point Cloud of Water-Conveyance Tunnel

Abstract

The inner-surface damage of water conveyance tunnels is the main hidden danger that threatens their safety and leads to serious accidents. The method based on the principle of acoustic reflection is the main means of inspecting damage to water-conveyance tunnels. However, affected by the tunnel environment and equipment noise, the obtained acoustic point cloud model inevitably suffers from noise, which can produce erroneous results. Therefore, we proposed a novel filtering method, called unsharp-mask-guided filtering for 3D point cloud, to reduce the impact of noise on the acoustic point cloud model of water-conveyance tunnels. The proposed method fuses the ideas of 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, edge-aware weighting mean is also used to retain the edge features of the point cloud model while smoothing the noise points. The experimental results show that our method can obtain impressive results and a better performance in both the acoustic point cloud model of the tunnel and the simulated point cloud model than many state-of-the-art methods.