3 D Urban Modeling from City - Scale Aerial Lidar Data

3D reconstruction from point clouds is a fundamental problem in both computer vision and computer graphics. As urban modeling is an important reconstruction problem that has various significant applications, this thesis investigates the complex problem of reconstructing 3D urban models from aerial LiDAR (Light Detection And Ranging) point cloud. In the first part of this thesis, an automatic urban modeling system is proposed which consists of three modules: (1) the classification module classifies input points into trees and buildings; (2) the segmentation module splits building points into different roof patches; (3) the modeling module creates building models, ground, and trees from point patches respectively. In order to support city-scale data sets, this pipeline is extended into an out-of-core streaming framework. By storing data as stream files on hard disks and using main memory as only a temporary storage for ongoing computation, an efficient out-of-core data management is achieved. City-scale urban models are successfully created from billions of points with limited computing resource. The second part of this thesis explores the 2.5D nature of building structures. The 2.5D characteristic of building models is observed and formally defined as “building structures are always composed of complex roofs and vertical walls”. Based on this observation, a 2.5D geometry representation is developed for the building structures, and used to extend a classic volumetric modeling approach into a 2.5D method, named 2.5D