散乱点云数据空间三角网构建方法的研究

点云数据三维建模主要是对目标物体的表面进行网格建模。三角形作为三维建模的基本表示元素,不仅性质简单,而且可以有效地表示物体表面复杂的几何属性。Delaunay三角网是当前使用最广泛的三角剖分方法,它能够最大限度地避免狭长三角形的产生,并且无论从何处开始建网都能保持网型的唯一性。本文在已有生长算法研究的基础上提出了一个新的算法:即在二维生长算法的基础上,利用空间三角形的法向量来进行第三点的搜索构建空间三角网。该算法的优点是:适合大量点云数据构建空间三角网、构建的空间三角网可以很好地反映出物体的表面特征。

Research of Scattered Point Cloud Data for Space Triangulation Construction Method

Point cloud data three - dimensional modeling is mainly focusing on the surface of the target object modeling grid. Triangle as the basic element of 3 dimension modeling, it is not only simple, but also can express geometrical surface complex properties effec-tively. Delaunay triangulation is currently the most widely used triangle subdivision method; it can maximally avoid the production of long and narrow triangle, it and can keep the uniqueness of network type no matter where to start creating network. On the basis of the existing grow algorithm study, this article proposed a new algorithm： That is on the basic of the 2 dimension grow algorithm, it uses the normal vector of space to seek the third point of the triangle and to create space triangulation. The advantage of this algorithm is ： it is not only suitable for a massive number of point cloud data to construct the space triangulation, but also reflect the object＇s surface characteristics of build space triangulation well.