Abstract: To mitigate the significant loss of important geographic entity structural features in mesh simplification algorithms based on quadratic error functions, this paper presents a mesh simplification method that integrates multi-source point cloud feature information. Firstly, laser point clouds and image dense matching point clouds are fused to enhance the quality of the mesh model. Subsequently, incorporating attributes such as color, elevation and curvature, a region-growing algorithm based on super-voxels is applied to segment the fused point cloud and extract feature information. Finally, the quadratic error matrix is updated using the extracted point cloud feature information to achieve high-precision mesh simplification. Utilizing a three-dimensional mesh constructed from the fused point cloud as experimental data, the proposed algorithm is evaluated and compared with QEM, QEF, and Low-poly algorithms. Experimental results indicate that the proposed method improves simplification accuracy by an average of 39.49%.

Key words: multi-source point cloud, mesh simplification, point cloud segmentation, quadric error metric, feature preserving