Lightweight 3D Modeling of Urban Buildings from Range Data

Abstract

Laser range scanners are widely used to acquire accurate scene measurements. The massive point clouds they generate, however, present challenges to efficient modeling and visualization. State-of-the-art techniques for generating 3D models from voluminous range data is well-known to demand large computational and storage requirements. In this paper, attention is directed to the modeling of urban buildings directly from range data. We present an efficient modeling algorithm that exploits a priori knowledge that buildings can be modeled from cross-sectional contours using extrusion and tapering operations. Inspired by this simple workflow, we identify key cross-sectional slices among the point cloud. These slices capture changes across the building facade along the principal axes. Standard image processing algorithms are used to remove noise, fill missing data, and vectorize the projected points into planar contours. Applying extrusion and tapering operations to these contours permits us to achieve dramatic geometry compression, making the resulting models suitable for web-based applications such as Google Earth or Microsoft Virtual Earth. This work has applications in architecture, urban design, virtual city touring, and online gaming. We present experimental results on synthetic and real urban building datasets to validate the proposed algorithm.