Algorithm for Reduction of Scattered Point Cloud Data Based on Curvature

For reduction of scattered point cloud data, one algorithm based on mean curvature criterion is put forward. The space partition of point cloud is generated using octree structure. k neighborhood is constructed by partition result. All the points in k neighborhood are approximated by quadratic parametric surface based on scattered point cloud parameterization. The mean curvature of fitting surface is calculated. The judgment of requiring reduction is decided by the average of mean curvature. Boundary points are identified and protected by curvature difference function. The results indicate that the proposed algorithm is of significance in theory and practice for reduction of point cloud with curvature diversification. The reliability and accuracy of the algorithm are proved by experimentation.