What Does the Sky Tell Us about the Camera?

  • Jean-François Lalonde
  • Srinivasa G. Narasimhan
  • Alexei A. Efros
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5305)


As the main observed illuminant outdoors, the sky is a rich source of information about the scene. However, it is yet to be fully explored in computer vision because its appearance depends on the sun position, weather conditions, photometric and geometric parameters of the camera, and the location of capture. In this paper, we propose the use of a physically-based sky model to analyze the information available within the visible portion of the sky, observed over time. By fitting this model to an image sequence, we show how to extract camera parameters such as the focal length, and the zenith and azimuth angles. In short, the sky serves as a geometric calibration target. Once the camera parameters are recovered, we show how to use the same model in two applications: 1) segmentation of the sky and cloud layers, and 2) data-driven sky matching across different image sequences based on a novel similarity measure defined on sky parameters. This measure, combined with a rich appearance database, allows us to model a wide range of sky conditions.


Azimuth Angle Camera Parameter Cloud Layer Horizon Line Camera Response Function 
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.


  1. 1.
    Weiss, Y.: Deriving intrinsic images from image sequences. In: IEEE International Conference on Computer Vision (2001)Google Scholar
  2. 2.
    Sunkavalli, K., Matusik, W., Pfister, H., Rusinkiewicz, S.: Factored time-lapse video. ACM Transactions on Graphics (SIGGRAPH 2007) 26(3) (August 2007)Google Scholar
  3. 3.
    Kim, S.J., Frahm, J.M., Polleyfeys, M.: Radiometric calibration with illumination change for outdoor scene analysis. In: IEEE Conference on Computer Vision and Pattern Recognition (2008)Google Scholar
  4. 4.
    Jacobs, N., Satkin, S., Roman, N., Speyer, R., Pless, R.: Geolocating static cameras. In: IEEE International Conference on Computer Vision (2007)Google Scholar
  5. 5.
    Jacobs, N., Roman, N., Pless, R.: Consistent temporal variations in many outdoor scenes. In: IEEE Conference on Computer Vision and Pattern Recognition (2007)Google Scholar
  6. 6.
    Sunkavalli, K., Romeiro, F., Matusik, W., Zickler, T., Pfister, H.: What do color changes reveal about an outdoor scene? In: IEEE Conference on Computer Vision and Pattern Recognition (2008)Google Scholar
  7. 7.
    Perez, R., Seals, R., Michalsky, J.: All-weather model for sky luminance distribution – preliminary configuration and validation. Solar Energy 50(3), 235–245 (1993)CrossRefGoogle Scholar
  8. 8.
    Yu, Y., Malik, J.: Recovering photometric properties of architectural scenes from photographs. Proceedings of ACM SIGGRAPH 1998 (July 1998)Google Scholar
  9. 9.
    Preetham, A.J., Shirley, P., Smits, B.: A practical analytic model for daylight. Proceedings of ACM SIGGRAPH 1999 (August 1999)Google Scholar
  10. 10.
    Jacobs, N., Roman, N., Pless, R.: Toward fully automatic geo-location and geo-orientation of static outdoor cameras. In: Workshop on applications of computer vision (2008)Google Scholar
  11. 11.
    Committee, C.T.: Spatial distribution of daylight – luminance distributions of various reference skies. Technical Report CIE-110-1994, International Commission on Illumination (1994)Google Scholar
  12. 12.
    Ineichen, P., Molineaux, B., Perez, R.: Sky luminance data validation: comparison of seven models with four data banks. Solar Energy 52(4), 337–346 (1994)CrossRefGoogle Scholar
  13. 13.
    Reda, I., Andreas, A.: Solar position algorithm for solar radiation applications. Technical Report NREL/TP-560-34302, National Renewable Energy Laboratory (November 2005)Google Scholar
  14. 14.
    Lalonde, J.F., Narasimhan, S.G., Efros, A.A.: Camera parameters estimation from hand-labelled sun positions in image sequences. Technical Report CMU-RI-TR-08-32, Robotics Institute. Carnegie Mellon University (July 2008)Google Scholar
  15. 15.
    Lin, S., Gu, J., Yamazaki, S., Shum, H.Y.: Radiometric calibration from a single image. In: IEEE Conference on Computer Vision and Pattern Recognition (2004)Google Scholar
  16. 16.
    Lalonde, J.F., Hoiem, D., Efros, A.A., Rother, C., Winn, J., Criminisi, A.: Photo clip art. ACM Transactions on Graphics (SIGGRAPH 2007) 26(3) (August 2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Jean-François Lalonde
    • 1
  • Srinivasa G. Narasimhan
    • 1
  • Alexei A. Efros
    • 1
  1. 1.School of Computer ScienceCarnegie Mellon University 

Personalised recommendations