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.