International Journal of Computer Vision

, Volume 99, Issue 1, pp 69–85 | Cite as

Creating Large-Scale City Models from 3D-Point Clouds: A Robust Approach with Hybrid Representation

Article

Abstract

We present a novel and robust method for modeling cities from 3D-point data. Our algorithm provides a more complete description than existing approaches by reconstructing simultaneously buildings, trees and topologically complex grounds. A major contribution of our work is the original way of modeling buildings which guarantees a high generalization level while having semantized and compact representations. Geometric 3D-primitives such as planes, cylinders, spheres or cones describe regular roof sections, and are combined with mesh-patches that represent irregular roof components. The various urban components interact through a non-convex energy minimization problem in which they are propagated under arrangement constraints over a planimetric map. Our approach is experimentally validated on complex buildings and large urban scenes of millions of points, and is compared to state-of-the-art methods.

Keywords

3D-modeling Shape representation Urban scenes Point data Energy minimization Markov Random Field 

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.INRIASophia AntipolisFrance
  2. 2.Université Paris EstSaint MandéFrance

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