A Highly Parallel Approach to Meshing Scattered Point Data

In this paper, we present a parallel mesh surface generation approach for unorganized point clouds that runs on the graphics processing unit (GPU). Our approach integrates point cloud simplification, point cloud optimization, and local triangulation techniques into the same framework. The input point cloud will be processed through three steps of algorithms, which are 1) preprocessing: to generate the neighborhood table of points and estimate the normal vectors, 2) clustering: to group points into optimized clusters that minimize the shape approximation error, and 3) meshing: to connect the seed points in clusters to form the resultant triangular mesh surface. As the number of clusters can be specified by users, the number of vertices on resultant mesh surfaces is controlled. The algorithms exploited here are highly parallelized to take advantage of the single-instruction-multiple-data (SIMD) parallelism that is available on consumer-level graphics hardware with GPU. Moreover, to overcome memory limitation on graphics hardware, the algorithms in all these steps are able to process massive data in streaming mode.