该方法适用于大规模点云点数较多且含有大量与球面形状无关的点。 使用该方

This method is suitable for large-scale point cloud contains a lot of points and more independent of the spherical shape points. When using this method, the relationship between the grid using Delaunay triangulation of the point cloud is divided criterion midpoint point, then regardless of the cloud point has been marked a spherical shape, the average distance of each point based on the point and its associated comparison between the standard distance and the global average distance variance determined. <br>Spherical marker is not used for parameter calculation, only when the correction of spherical and aspherical parameters corresponding to the last given. <br>Experimental results spherical mass point cloud point of impact is independent of the method can effectively suppress, detect faster.

This method is suitable for large-scale point cloud points with a large number of points independent of spherical shape. With this method, the Delaunay triangulation criterion is used to divide the grid relationship between the point cloud midpoint and point, and then the points in the point cloud that are independent of the spherical shape are marked, based on the comparison between the average distance of each point and its connected points and the standard distance determined by the global average distance and variance.<br>Marker points are not used for spherical parameter calculations, only when the corresponding spherical and correction spherical parameters are given at the end.<br>The experimental results verify that the spherical shape of large-scale point cloud has an independent point effect and the method can be effectively suppressed and the detection speed is faster.

This method is suitable for large-scale point clouds with many points and a large number of points independent of spherical shape. When using this method, Delaunay triangulation rule is used to divide the grid relationship between the points in the point cloud, and then the points in the point cloud that are independent of the spherical shape are marked, based on the comparison between the average distance of each point and its connected points and the standard distance determined by the global average distance and variance.<br>The marked points are not used in the calculation of spherical parameters, but only when the corresponding spherical surface and the modified spherical parameters are given at the end.<br>Experimental results show that this method can effectively suppress the influence of spherical shape independent points in large-scale point cloud, and the detection speed is faster.