基于GPU并行三角化的点云模型快速重建方法

A Rapid Method of Point Cloud Model Reconstruction Based on GPU Parallel Triangulation

如何基于点云数据进行三维模型的快速重建已经成为研究的热点问题之一。本文首先介绍了Delaunay三角化的相关理论基础,总结了3种Delaunay三角网构建方法及各自优缺点;其次,提出GPU点云数据快速三角化方法,从分治法构建点云Delaunay三角网入手,提出并行自适应点云数据分块方法,将点云划分为若干个数据子集,并构建空间二叉树结构,在此基础上,实现并行三角化操作,并根据建立的二叉树结构,完成了子集三角网的合并,得到最终的点云Delaunay三角格网模型;最后,分别对7种不同密度的大规模点云数据进行了模型重建试验,相比于传统CPU方法,本文GPU方法能够将模型重建效率提高数十倍甚至上百倍,大大提高了模型重建的速度。

Nowadays,the requirement of 3D model is increasing owing to the high-speed development of the society,it has been a hot issue to reconstruct 3 D model rapidly from point cloud.The basis of Delaunay triangulation is discussed,and three methods to construct the Delaunay mesh are expounded,respectively,accompanied by the analysis of their merit and demerit. A new method of rapid triangulation for point cloud on GPU is put forward.To execute the proposed triangulation,an approach for parallel subdivision adaptively for point cloud is carried out firstly to obtain many subsets of the data,and a binary tree is then built for the subdivision. Parallel triangulation of the subsets is accomplished with the divide-and-conquer strategy,and the final whole mesh is obtained by merging the adjacent subset-meshes based on the binary tree.In addition,experiments of reconstruction for seven sets of dense TLS point clouds with different densities have been implemented.The proposed GPU-based method could enhance the efficiency of 3D reconstruction of dozens of times and even one hundred times,compared with the traditional CPU-based method.