Point Cluster Analysis Using a 3D Voronoi Diagram with Applications in Point Cloud Segmentation
Point Cluster Analysis Using a 3D Voronoi Diagram with Applications in Point Cloud Segmentation
复制标题
使用 3D Voronoi 图进行点聚类分析及其在点云分割中的应用
DOI：
10.3390/ijgi4031480
复制
发表时间：
2015-09-01
影响因子：
3.4
通讯作者：
Mao, Zhengyuan
中科院分区：
文献类型：
--
作者：
Ying, Shen;Xu, Guang;Mao, Zhengyuan
三维（3D）点分析和可视化是地理空间数据集中点簇检测和分割的最有效方法之一。然而，严重的散射和凝固特性会干扰 3D 点簇的视觉检测。为了克服这个问题，本研究提出使用 3D Voronoi 图来分析和可视化 3D 点，而不是原始数据项。所提出的算法通过应用一组 3D Voronoi 单元来描述和量化 3D 点来计算 3D 点簇。 3D 模型点云的分解由 3D Voronoi 单元参数指导。参数值从 Voronoi 单元映射到 3D 点，以显示空间模式和关系；因此，3D点簇图案可以突出显示并易于识别。为了捕获不同的聚类模式，测试了连续渐进的聚类和分段。显示 3D 空间关系以促进聚类检测。此外，利用真实 3D 数据案例生成的分割来证明我们的方法在检测连续点云分割的不同空间簇方面的可行性。
Three-dimensional (3D) point analysis and visualization is one of the most effective methods of point cluster detection and segmentation in geospatial datasets. However, serious scattering and clotting characteristics interfere with the visual detection of 3D point clusters. To overcome this problem, this study proposes the use of 3D Voronoi diagrams to analyze and visualize 3D points instead of the original data item. The proposed algorithm computes the cluster of 3D points by applying a set of 3D Voronoi cells to describe and quantify 3D points. The decompositions of point cloud of 3D models are guided by the 3D Voronoi cell parameters. The parameter values are mapped from the Voronoi cells to 3D points to show the spatial pattern and relationships; thus, a 3D point cluster pattern can be highlighted and easily recognized. To capture different cluster patterns, continuous progressive clusters and segmentations are tested. The 3D spatial relationship is shown to facilitate cluster detection. Furthermore, the generated segmentations of real 3D data cases are exploited to demonstrate the feasibility of our approach in detecting different spatial clusters for continuous point cloud segmentation.