Point Cloud Clustering Using Panoramic Layered Range Image

Abstract

Point-cloud clustering is an essential technique for modeling massive point clouds acquired with a laser scanner. There are three clustering approaches in point-cloud clustering, namely model-based clustering, edge-based clustering, and region-based clustering. In geoinformatics, edge-based and region-based clustering are often applied for the modeling of buildings and roads. These approaches use low-resolution point-cloud data that consist of tens of points or several hundred points per m2, such as aerial laser scanning data and vehicle-borne mobile mapping system data. These approaches also focus on geometrical knowledge and restrictions. We focused on region-based point-cloud clustering to improve 3D visualization and modeling using massive point clouds. We proposed a point-cloud clustering methodology and point-cloud filtering on a multilayered panoramic range image. A point-based rendering approach was applied for the range image generation using a massive point cloud. Moreover, we conducted three experiments to verify our methodology