3D Building Reconstruction from LIDAR Data

  • Yuan Luo
  • Marina L. Gavrilova
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3980)


As a fast data acquisition technique, Light Detection and Ranging (LIDAR) can be widely used in many applications, such as visualization, GIS and mobile communication. Since manual surface reconstruction is very costly and time consuming, the development of automated algorithms is of great importance. In this paper a fully automated technique to extract urban building models from LIDAR data is presented. First, LIDAR points are re-sampled into regular grids with the optimal pixel size. After filling holes in the range image, Delaunay Triangulation is utilized to generate 3D triangle meshes. Building height mask is then applied to extract building roof points. Finally, a geometric primitive-based fitting approach is adopted to verify and refine the reconstructed models. The algorithm is tested on two buildings from a locally acquired LIDAR data set. The results indicate that our approach is suitable for automatically producing urban building models from LIDAR data.


Delaunay Triangulation Lidar Data Digital Surface Model Laser Data Delaunay Tessellation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yuan Luo
    • 1
  • Marina L. Gavrilova
    • 1
  1. 1.Department of Computer ScienceUniversity of CalgaryCalgaryCanada

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