密集散乱数据三角划分及数据压缩

介绍了Delaunay三角划分方法，运用一种算法对密集散乱数据进行三角划分，并在给定容差条件下对其进行成批压缩。由点云数据中最大、最小的六个点构成初始八面体，将数据分割为八个星角形区域上的凸包数据，在每个凸包数据中，搜寻到其对应的三角形的垂直距离最大的点，如果距离大于给定的容差，则将该点插入并局部优化三角网格。反之则将该点压缩掉。最后给出的实例证明了该算法的有效性和效率。

Triangulation and data reduction of Scattered 3D Points

This paper introduces a delaunay triangulation and describes an adaptive algorithms for triangulation and data reduction from scattered 3D points in the given error The method begins with octahedron made of the maximum and minimum six points If the maximum error point is greater than given error, it refines grid by adding the maximum error point at a time among the each internal points until the desired triangulation accuracy is reached Experimental results are given to illustrate it's efficiency