Part-Based Modeling of Pole-Like Objects Using Divergence-Incorporated 3-D Clustering of Mobile Laser Scanning Point Clouds

Abstract

3-D digital city vividly presents a real-world city and has been widely needed for many application domains. Numerous pole-like objects (PLOs), including trees, street lamps, and traffic signs, are an indispensable part of 3-D digital city. The point cloud data of mobile laser scanning (MLS) systems can capture both the geometric shape and geospatial coordinates of the PLOs while moving along the roads. This article is motivated to accurately extract and efficiently model PLOs from the point cloud data. The main contributions of this article are as follows: 1) a divergence-incorporated clustering algorithm is proposed to extract trunks accurately from the pole-like 3-D distribution perspective of point cloud; 2) an adaptive growing strategy of alternately extending and updating 3-D neighbors is proposed to get the complete canopy points of various shapes and density; and 3) the part-based modeling is proposed to synthesize the point cloud of PLOs with meaningful 3-D shapes, providing a way to model objects for the 3-D digital city vividly and efficiently. The proposed method is tested on three data sets with different interference, shape of the canopy, and point density. Experimental results demonstrate that the proposed method can extract and model the PLOs effectively and efficiently for 3-D digital city. The precision of trunk extraction is 98.45%, 98.08%, and 92.39%, the completeness of canopy extraction is 80.54%, 89.84%, and 89.29%, and the modeling time for a PLO is 0.011, 0.038, and 0.063 s in three data sets.