Advertisement

Reconstructing the World’s Museums

  • Jianxiong Xiao
  • Yasutaka Furukawa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7572)

Abstract

Photorealistic maps are a useful navigational guide for large indoor environments, such as museums and businesses. However, it is impossible to acquire photographs covering a large indoor environment from aerial viewpoints. This paper presents a 3D reconstruction and visualization system to automatically produce clean and well-regularized texture-mapped 3D models for large indoor scenes, from ground-level photographs and 3D laser points. The key component is a new algorithm called “Inverse CSG” for reconstructing a scene in a Constructive Solid Geometry (CSG) representation consisting of volumetric primitives, which imposes powerful regularization constraints to exploit structural regularities. We also propose several techniques to adjust the 3D model to make it suitable for rendering the 3D maps from aerial viewpoints. The visualization system enables users to easily browse a large scale indoor environment from a bird’s-eye view, locate specific room interiors, fly into a place of interest, view immersive ground-level panorama views, and zoom out again, all with seamless 3D transitions. We demonstrate our system on various museums, including the Metropolitan Museum of Art in New York City – one of the largest art galleries in the world.

Keywords

Markov Random Field Wall Model Laser Point Indoor Scene Constructive Solid Geometry 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Project website (2012), http://mit.edu/jxiao/museum
  2. 2.
    Curless, B., Levoy, M.: A volumetric method for building complex models from range images. In: SIGGRAPH, pp. 303–312 (1996)Google Scholar
  3. 3.
    Li, Y., Wu, X., Chrysathou, Y., Sharf, A., Cohen-Or, D., Mitra, N.J.: Globfit: Consistently fitting primitives by discovering global relations. In: SIGGRAPH (2011)Google Scholar
  4. 4.
    Liu, T., Carlberg, M., Chen, G., Chen, J., Kua, J., Zakhor, A.: Indoor localization and visualization using a human-operated backpack system. In: IPIN (2010)Google Scholar
  5. 5.
    Sanchez, V., Zakhor, A.: Planar 3d modeling of building interiors from point cloud data. In: ICIP (2012)Google Scholar
  6. 6.
    Agarwal, S., Furukawa, Y., Snavely, N., Simon, I., Curless, B., Seitz, S.M., Szeliski, R.: Building Rome in a day. Communications of the ACM (2011)Google Scholar
  7. 7.
    Huang, Q.X., Anguelov, D.: High quality pose estimation by aligning multiple scans to a latent map. In: ICRA (2010)Google Scholar
  8. 8.
    Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D.: RGB-D mapping: Using depth cameras for dense 3d modeling of indoor environments. In: ISER (2010)Google Scholar
  9. 9.
    Furukawa, Y., Curless, B., Seitz, S.M., Szeliski, R.: Reconstructing building interiors from images. In: ICCV (2009)Google Scholar
  10. 10.
    Hernández Esteban, C., Vogiatzis, G., Cipolla, R.: Probabilistic visibility for multi-view stereo. In: CVPR (2007)Google Scholar
  11. 11.
    Xiao, J., Fang, T., Zhao, P., Lhuillier, M., Quan, L.: Image-based street-side city modeling. In: SIGGRAPH Asia (2009)Google Scholar
  12. 12.
    Suveg, I., Vosselman, G.: Reconstruction of 3d building models from aerial images and maps. ISPRS Journal of Photogrammetry and Remote Sensing (2004)Google Scholar
  13. 13.
    Uyttendaele, M., Criminisi, A., Kang, S.B., Winder, S., Szeliski, R., Hartley, R.: Image-based interactive exploration of real-world environments. CGA (2004)Google Scholar
  14. 14.
    CGAL: Computational Geometry Algorithms Library (2012), http://www.cgal.org
  15. 15.
    Sinha, S.N., Steedly, D., Szeliski, R., Agrawala, M., Pollefeys, M.: Interactive 3D architectural modeling from unordered photo collections. In: SIGGRAPH Asia (2008)Google Scholar
  16. 16.
    Delong, A., Boykov, Y.: A scalable graph-cut algorithm for n-d grids. In: CVPR (2008)Google Scholar
  17. 17.
    Garland, M.: Qslim: Quadric-based simplification algorithm (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jianxiong Xiao
    • 1
  • Yasutaka Furukawa
    • 2
  1. 1.Massachusetts Institute of TechnologyUSA
  2. 2.Google Inc.USA

Personalised recommendations