Reducing the number of points without sacrificing the quality of the original point cloud data is an essential preprocessing step for downsizing storage space and lowering calculation intensity.This study proposes an adaptive simplification method for underwater 3D sonar point cloud data.The normal differential operator is determined to identify sudden changes in the geometric scale of the point cloud,enabling segmenta-tion of the boundary and primary part of the original point cloud.For the boundary part of the point cloud,the moving least squares method is ap-plied to optimize the boundary points,reduce noise influence,and maintain the geometric consistency of the surface.The octree is used for down-sampling on the boundary based on the point cloud's voxel grid structure.Depending on this structure,local farthest point sampling is implemen-ted,achieving uniform simplification while ensuring the isotropy of the boundary part of the simplified point cloud and effectively retaining the geometric feature information of the boundary part of the point cloud.For the main part of the point cloud,the voxel center sampling method is employed to reduce the data and maintain the overall isotropy of the simplified point cloud,and the point cloud surface is smoothed through Gaus-sian filtering.Finally,the simplified boundary and main body are integrated to obtain the final simplified results.Experiments showed that the proposed simplification method has a low computational cost and fast processing speed,improving the simplification speed of underwater point cloud data by approximately 32％while maintaining the same simplification rate as current typical algorithms.In addition,the optimization of the algorithm on the boundary points and overall distribution of the underwater 3D point cloud is demonstrated through surface density comparison and geometric distortion analysis.The method enhances the detection efficiency of underwater operational targets and produces simplified under-water mission target point clouds that retain critical geometric feature information and remain isotropic.
underwater point cloud3D sonarpoint cloud simplificationnormal differentiationvoxel grid
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