在现有Delaunay三角网生长法的基础上进行改进,提出了一种三角网生长算法.该算法对大规模点云进行等格网分块,自适应确定搜索范围.通过在构建过程中对生成的基线进行分组和排序,动态删除封闭点,提高了构建三角网的速度;通过在整个点集范围内进行搜索,避免了通过插值所产生的误差和模块之间的拼接过程.利用此算法对大规模LiDAR点云数据进行构网,结果表明了该算法的有效性.

Based on the existing Delaunay triangulation method, an algorithm of triangulation growth is presented. This algorithm divides the large-scale point clouds into uniform grids and determines the searching scope self-adaptively. During the process of building a triangulated irregular network (TIN) model, the generated base-lines in groups are grouped, and the close-points are removed dynamically, which improved the speed of reconstructing TIN in large-scale scenes dramatically. By searching the triangular vertices in the scope of the whole data set, this method avoided errors caused by interpolation and the process of stitching between grids. The efficiency and effectiveness of this algorithm are verified by using real world data to build TIN model with large scale LiDAR point clouds.

Supported by the International (Regional) Cooperation and Exchange Project in National Natural Science Foundation of China under Grant No.60573174 (国家自然科学基金国际(地区)合作交流项目); the National Natural Science Foundation of China under Grant No.60673028 (国家自然科学基金); the Open Project Foundation in Key Laboratory of Special Display Technology for the Ministry of Education of China (特种显示技术教育部重点实验室开放课题)