散乱点云数据的曲率精简算法

Algorithm for Reduction of Scattered Point Cloud Data Based on Curvature

针对海量散乱点云数据精简问题,提出了以平均曲率为判据的精简算法.采用八叉树结构对点云数据进行空间分割,由分割结果建立k邻域.在散乱数据点参数化的基础上,对k邻域内的散乱点进行二次曲面拟合,求出拟合曲面的平均曲率,进而得出邻域内所有数据点的平均曲率均值,以此为判据进行数据精简.构造曲率差函数,识别出边界数据点,对其进行数据保护.结果表明,该算法对具有曲率多样化特点的点云数据精简具有一定的理论意义和应用价值.通过实验验证了该算法的可靠性和准确性.

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.