基于多维欧氏空间相似度的激光点云分割方法

Segmentation of LiDAR point clouds based on similarity measures in multi-dimensional Euclidean Space

点云数据分割是对激光扫描(LiDAR)场景进行三维重建的基础。针对现有基于边界、表面或聚类的点云分割方法中存在的分割不足或过度分割问题,提出了一种基于多维欧几里德空间相似度的点云数据分割方法。通过计算激光点的法向量,结合点云的光谱特征进行数学变换,计算激光点在多维空间中的欧氏距离,比较邻近点间的相似性,最终完成对激光点云数据的分割。该方法解决了常用点云分割中几何特征和光谱特征无法同时使用的问题,融合了几何分割和颜色分割的两方面优势,提高了点云分割精度。采用2组数据分别比较了基于几何特征、光谱特征和多维空间相似度的3种不同分割算法的分割结果,实验结果验证了该方法的可行性和实用性。

The segmentation of LiDAR point cloud is a basic and key step in 3D reconstruction of architecture. Some problems such as under-segmentation or over-segmentation exist in current point cloud segmentation based on boundary, surface or clustering method. In this paper, a point data segmentation method based on similarity measures in multi-dimension Euclidean Space（ SMMES） is presented. The main workflow of this method consists of calculating point normal vector, transforming the raw data combined with image features, calculating Euclidean distance in the multi-dimension space, comparing the similarity between the adjacent points,and segmenting the point data. The method proposed in this paper has solved the problem that geometry and spectral features cannot be used in parallel during the point cloud segmentation. In addition, it has the advantages of both geo -metrical segmentation and color-metrical segmentation, and can improve the accuracy of the point cloud segmentation. The segmentation results of the three different methods which are based on geometry features, spectral features and SMMES respectively were compared with each other by using two sets of data, and the experimental results show that the proposed method is significantly feasible and practical.