基于自适应角度的三维点云分割方法

3D Point Cloud Segmentation Method Based on Adaptive Angle

点云分割是基于点云数据空间几何信息提取的一项重要工作,它是点云数据特征提取与分析的基础。同时,点云数据通常是离散的和非结构化的,点云数据的分割不是一项简单的数据处理任务,分割效率和分割精度决定了后续数据处理工作的结果。因此,研究点云数据分割具有重要意义。提出一种基于自适应角度的三维点云切割算法,使用PCA算法找到最佳降维投射方向,以降低原始点云数据维度,并利用投射簇的概念实现对原始目标点云的切割获取。

Point cloud of 3D segmentation is an impor tant task to extract the point cloud data space based on geometric information. It is the basis of point cloud data feature extraction and analysis. At the same t ime, the point cloud data are usually discrete and unstructured. The segmentation of point cloud data is not a simple data processing task, and the segmentation efficiency and accuracy determine the data processing results of the work. Therefore, the research of point cloud data segmentation has important significance. This paper presented a cut t ing algorithm for 3D point cloud based on adaptive angle, in which the PCA algorithm is used to find the optimal dimension projection direction to reduce the dimension of the original point cloud data,and the concept of projection cluster is used to obtain cut t ing of the original target point cloud.