The Point Cloud Simplification Algorithm Based on Three Feature-Preserving BoundariesPoint Cloud SimplificationThree Feature-Preserving Boundaries

Abstract

The point cloud simplification process easily removes edge points, resulting in the loss of point cloud edge features. To address this issue, a point cloud simplification algorithm based on three feature-preserving boundaries is proposed. Firstly, a dynamic grid method is used to search for neighboring points of data points. Then, if there is a hole on the surface of the point cloud, calculate the maximum angle of the adjacent adjacent points around the point on the tangent plane of the data point, and set a threshold to extract the boundary point of the point cloud hole. Next, the number of points within a certain radius of the data point and the average curvature of points under different search radii are calculated. Feature thresholds are defined to extract point cloud edge points. Finally, the remaining non-edge points are simplified based on voxel structure, and the extracted edge points are merged with the simplified non-edge points to achieve simplification. Experimental results show that the proposed algorithm effectively preserves the edge features of the point cloud.