Point Cluster Analysis Using a 3D Voronoi Diagram with Applications in Point Cloud Segmentation

Shen Ying,Chengpeng Li,Zhengyuan Mao,Guang Xu

doi:10.3390/ijgi4031480

Abstract

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.

Highlights

Introduction and Literature ReviewThree-dimensional (3D) point clouds are widely used in various applications, such as real-time surveying and 3D modeling, as the raw data was collected and stored in this form
The parameters exert different influences on the point clusters based on the 3D Voronoi diagram
It is apparent that the segmentations depend on the clustering of 3D point distributions, which is determined by three parameters: distance, importance value and spatial neighborhood relationship

Summary

Introduction

Three-dimensional (3D) point clouds are widely used in various applications, such as real-time surveying and 3D modeling, as the raw data was collected and stored in this form. Decomposing 3D models into meaningful parts has been an increasingly important topic in the shape analysis community. Point clouds are typically divided into two types: strip surfaces, e.g., laser scans, and 3D scatter points, e.g., the stars in the sky. For the former, there is only one point in one direction (similar to digital elevation models (DEMs)); for the latter, there can be more points in one direction at different locations, for example, a ray intersecting a 3D sculpture model would generate at least two points. Spatial point analysis in a 3D environment is a powerful technique applied to spatial particles (the 3D generating point is called a particle in this study) with spatial locations observed within a certain region

Objectives

Discussion

Conclusion