Point Cloud Simplification Algorithm for Terrain Data
1 Foshan Urban Planning Design and Surveying Research Institute, 62 North-Lingnan Road, Foshan 528000,China
2 School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079,China
Abstract Large amounts of data will have an adversely impact on point cloud’s processing, storage, transmission and display. In order to improve the point cloud’s data processing efficiency, it is necessary to simply point cloud data and reduce data redundancy. Due to the large quantity and complex surface features of terrain point cloud data, the point cloud simplification algorithm for terrain data is proposed.We use K-D Tree to search the point’s K neighborhood and the moving least squares method to calculate the curvature of each point. Through the vision of curvature, point cloud data is simplified based on distance in the flat area to improve the efficiency of the algorithm and on the basis of curvature in the steep area to retain the feature information. According to the quantitative evaluation method,based on the entropy theory and verified by the example, it is indicated that the proposed new simplification algorithm for terrain data can simply the data in high-fidelity with high-precision. It is highly efficient feasible and universally applicable.
