曲面重构中散乱点云数据曲率估算算法的研究

Curvature estimation arithmetic for scatted data point cloud in surface reconstruction

获取测量点云数据的几何特征信息是曲面重构的基础,估算数据点方向矢量和曲率是点云数据处理中必须面对的问题。这里针对散乱测量数据点云,以局部数据点协方差矩的最小特征向量作为数据点的方向矢量,并根据实际测量情况,对基于二次曲面拟合的数据点曲率估算算法进行了改进。对实际测量点云数据,能够较准确地估算出点云方向矢量和曲率,并能形象显示出数据点云的曲率分布。

The acquisition of the geometric feature information for the measured data point cloud is the foundation of the surface reconstructoin, and the estimation for normal vector and curvature is the key problem in the processing of the point cloud. Based on the scatted data, a suitable method has been proposed in this paper, in which the least eigenvector of covariance matrix for the local data points is used as the normal vector of the data .point. The arithmetic for curvature estimation has also been improved according to the measured data. It has been proved in practice that by applying the improved arithmetic, the normal vector and curvature are accurately estimated, and the curvature distribution of data point is display visually.