基于三维形状匹配的点云分割

Point Cloud Segmentation Based on Three-Dimensional Shape Matching

随着三维扫描技术的迅猛发展,点云数据的数据量变得异常庞大,这对点云计算的性能提出了更高的要求。因此,如何有效提高算法的执行效率一直是该领域的研究热点和难点。日益增大的数据量隐藏了丰富的三维(3D)形状模型,将形状模型参与到点云计算过程中,为提高点云计算的执行效率提供了一种新的方法和思路。利用3D几何特征分析技术,获取与形状相关的特征参数,并使其参与到点云分割过程中,提出了形状分割方法。利用八叉树算法组织点云数据,发现数据之间的相邻关系,依靠点云数据的密度自适应地双向线性调整八叉树并建立数据索引。使用规则图形建立3D形状模型库,实现模型与分割区域的匹配,进而提取分割区域的形状参数,为提高点云数据计算的精度和速度奠定基础。在分割效果和分割时间上,对比了不同算法,验证了基于形状的点云分割算法的可行性以及稳健性。

With the rapid development of three-dimensional(3D)scanning technique,the huge volume of point cloud has been produced,which puts forward higher requirements for the performance of point cloud computing.Therefore,how to improve the efficiency of the algorithm has become a hot topic in this field.There are rich 3D shape models hidden in the ever-increasing amount of point cloud data.Inspired by the relationship between 3D shape models and the point cloud,we provide a new method to improve the execution efficiency of algorithms about point cloud computing.The 3D geometric feature analysis technology is used to obtain shape-related feature parameters,and the point cloud segmentation algorithm is proposed based on it.We use octree algorithm to organize point cloud and obtain the neighbor relationship.A self adaptive and dual linear octree algorithm is designed based on the density of point clouds to establish the data index.We build a 3D shape library by using regular shape models,and realize the algorithm for matching models with data segmentation regions.Further,we extract the shape parameters of the segmented region,which are the foundation for improving the accuracy and speed of point cloud data processing.Moreover,the segmentation effectiveness and time performance of different algorithms are compared,and the experimental results indicate that the proposed algorithm is feasible and robust.