2.5D Dual Contouring: A Robust Approach to Creating Building Models from Aerial LiDAR Point Clouds

  • Qian-Yi Zhou
  • Ulrich Neumann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6313)


We present a robust approach to creating 2.5D building models from aerial LiDAR point clouds. The method is guaranteed to produce crack-free models composed of complex roofs and vertical walls connecting them. By extending classic dual contouring into a 2.5D method, we achieve a simultaneous optimization over the three dimensional surfaces and the two dimensional boundaries of roof layers. Thus, our method can generate building models with arbitrarily shaped roofs while keeping the verticality of connecting walls. An adaptive grid is introduced to simplify model geometry in an accurate manner. Sharp features are detected and preserved by a novel and efficient algorithm.


Point Cloud Principal Direction Leaf Cell Input Point Boundary Polygon 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Supplementary material (14.6 mb)
Electronic Supplementary Material (14,922 KB)


  1. 1.
    Curless, B., Levoy, M.: A volumetric method for building complex models from range images. In: ACM SIGGRAPH (1996)Google Scholar
  2. 2.
    Fiocco, M., Boström, G., Gonçalves, J.G.M., Sequeira, V.: Multisensor fusion for volumetric reconstruction of large outdoor areas. 3DIM (2005)Google Scholar
  3. 3.
    Google: Google 3d warehouse,
  4. 4.
    Ju, T., Losasso, F., Schaefer, S., Warren, J.: Dual contouring on hermite data. In: ACM SIGGRAPH (2002)Google Scholar
  5. 5.
    Lafarge, F., Descombes, X., Zerubia, J., Pierrot-Deseilligny, M.: Building reconstruction from a single dem. In: CVPR (2008)Google Scholar
  6. 6.
    Lindstrom, P.: Out-of-core simplification of large polygonal models. In: ACM SIGGRAPH (2000)Google Scholar
  7. 7.
    Lorensen, W., Cline, H.: Marching cubes: A high resolution 3d surface construction algorithm. In: ACM SIGGRAPH (1987)Google Scholar
  8. 8.
    Matei, B., Sawhney, H., Samarasekera, S., Kim, J., Kumar, R.: Building segmentation for densely built urban regions using aerial lidar data. In: CVPR (2008)Google Scholar
  9. 9.
    Pauly, M.: Point primitives for interactive modeling and processing of 3d geometry. PhD thesis, ETH Zurich (2003)Google Scholar
  10. 10.
    Poullis, C., You, S.: Automatic reconstruction of cities from remote sensor data. In: CVPR (2009)Google Scholar
  11. 11.
    Verma, V., Kumar, R., Hsu, S.: 3d building detection and modeling from aerial lidar data. In: CVPR (2006)Google Scholar
  12. 12.
    You, S., Hu, J., Neumann, U., Fox, P.: Urban site modeling from lidar. In: Kumar, V., Gavrilova, M.L., Tan, C.J.K., L’Ecuyer, P. (eds.) ICCSA 2003. LNCS, vol. 2669, pp. 579–588. Springer, Heidelberg (2003)Google Scholar
  13. 13.
    Zebedin, L., Bauer, J., Karner, K., Bischof, H.: Fusion of feature- and area-based information for urban buildings modeling from aerial imagery. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 873–886. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  14. 14.
    Zhou, Q.Y., Neumann, U.: Fast and extensible building modeling from airborne lidar data. In: ACM GIS (2008)Google Scholar
  15. 15.
    Zhou, Q.Y., Neumann, U.: A streaming framework for seamless building reconstruction from large-scale aerial lidar data. In: CVPR (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Qian-Yi Zhou
    • 1
  • Ulrich Neumann
    • 1
  1. 1.University of Southern California 

Personalised recommendations