Modeling Dynamic Scenes Recorded with Freely Moving Cameras

  • Aparna Taneja
  • Luca Ballan
  • Marc Pollefeys
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6494)


Dynamic scene modeling is a challenging problem in computer vision. Many techniques have been developed in the past to address such a problem but most of them focus on achieving accurate reconstructions in controlled environments, where the background and the lighting are known and the cameras are fixed and calibrated. Recent approaches have relaxed these requirements by applying these techniques to outdoor scenarios. The problem however becomes even harder when the cameras are allowed to move during the recording since no background color model can be easily inferred.

In this paper we propose a new approach to model dynamic scenes captured in outdoor environments with moving cameras. A probabilistic framework is proposed to deal with such a scenario and to provide a volumetric reconstruction of all the dynamic elements of the scene.

The proposed algorithm was tested on a publicly available dataset filmed outdoors with six moving cameras. A quantitative evaluation of the method was also performed on synthetic data. The obtained results demonstrated the effectiveness of the approach considering the complexity of the problem.


Color Information Foreground Object Dynamic Scene Background Geometry Dynamic Element 
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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  1. 1.
    Kim, H., Sarim, M., Takai, T., Guillemaut, J.Y., Hilton, A.: Dynamic 3d scene reconstruction in outdoor environments. In: 3DPVT (2010)Google Scholar
  2. 2.
    Guan, L., Franco, J.S., Pollefeys, M.: Multi-object shape estimation and tracking from silhouette cues. In: CVPR (2008)Google Scholar
  3. 3.
    Franco, J.S., Boyer, E.: Fusion of multi-view silhouette cues using a space occupancy grid. In: ICCV, pp. 1747–1753 (2005)Google Scholar
  4. 4.
    Furukawa, Y., Ponce, J.: Dense 3d motion capture for human faces. In: CVPR, pp. 1674–1681 (2009)Google Scholar
  5. 5.
    Tung, T., Nobuhara, S., Matsuyama, T.: Complete multi-view reconstruction of dynamic scenes from probabilistic fusion of narrow and wide baseline stereo. In: ICCV (2009)Google Scholar
  6. 6.
    Seitz, S., Curless, B., Diebel, J., Scharstein, D., Szeliski, R.: A comparison and evaluation of multi-view stereo reconstruction algorithms. In: CVPR (2006)Google Scholar
  7. 7.
    Seitz, S.M., Dyer, C.R.: Photorealistic scene reconstruction by voxel coloring. In: CVPR, p. 1067 (1997)Google Scholar
  8. 8.
    Vlasic, D., Peers, P., Baran, I., Debevec, P., Popović, J., Rusinkiewicz, S., Matusik, W.: Dynamic shape capture using multi-view photometric stereo. In: SIGGRAPH Asia (2009)Google Scholar
  9. 9.
    Ahmed, N., Theobalt, C., Dobrev, P., Seidel, H.P., Thrun, S.: Robust fusion of dynamic shape and normal capture for high-quality reconstruction of time-varying geometry. In: CVPR (2008)Google Scholar
  10. 10.
    Vedula, S., Baker, S., Seitz, S., Kanade, T.: Shape and motion carving in 6d. In: CVPR (2000)Google Scholar
  11. 11.
    Matusik, W., Buehler, C., Raskar, R., Gortler, S.J., McMillan, L.: Image-based visual hulls. In: SIGGRAPH, pp. 369–374. ACM Press, New York (2000)Google Scholar
  12. 12.
    Goldlucke, B., Ihrke, I., Linz, C., Magnor, M.: Weighted minimal hypersurface reconstruction. PAMI, 1194–1208 (2007)Google Scholar
  13. 13.
    Hilton, A., Starck, J.: Multiple view reconstruction of people. In: 3DPVT (2004)Google Scholar
  14. 14.
    Sinha, S.N., Pollefeys, M.: Multi-view reconstruction using photo-consistency and exact silhouette constraints: A maximum-flow formulation. In: ICCV, pp. 349–356 (2005)Google Scholar
  15. 15.
    Hasler, N., Rosenhahn, B., Thormahlen, T., Wand, M., Gall, J., Seidel, H.P.: Markerless motion capture with unsynchronized moving cameras. In: CVPR (2009)Google Scholar
  16. 16.
    Ballan, L., Brostow, G.J., Puwein, J., Pollefeys, M.: Unstructured video-based rendering: Interactive exploration of casually captured videos. SIGGRAPH (2010)Google Scholar
  17. 17.
    Baumberg, A., Hogg, D.: An efficient method for contour tracking using active shape models. In: Motion of Non-Rigid and Articulated Objects, pp. 194–199 (1994)Google Scholar
  18. 18.
    Leibe, B., Cornelis, N., Cornelis, K., Gool, L.V.: Dynamic 3d scene analysis from a moving vehicle. In: CVPR (2007)Google Scholar
  19. 19.
    Elgammal, A., Duraiswami, R., Harwood, D., Davis, L.S.: Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proceedings of the IEEE 90, 1151–1163 (2002)CrossRefGoogle Scholar
  20. 20.
    Sheikh, Y., Javed, O., Kanade, T.: Background subtraction for freely moving cameras. In: ICCV (2009)Google Scholar
  21. 21.
    Ivanov, Y., Bobick, A., Liu, J.: Fast lighting independent background subtraction. International Journal of Computer Vision 37, 199–207 (2000)CrossRefzbMATHGoogle Scholar
  22. 22.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004) ISBN: 0521540518CrossRefzbMATHGoogle Scholar
  23. 23.
    Pollefeys, M., Van Gool, L., Vergauwen, M., Verbiest, F., Cornelis, K., Tops, J., Koch, R.: Visual modeling with a hand-held camera. IJCV 59, 207–232 (2004)CrossRefGoogle Scholar
  24. 24.
    Zach, C., Pock, T., Bischof, H.: A globally optimal algorithm for robust TV-L 1 range image integration. In: ICCV (2007)Google Scholar
  25. 25.
    Zhang, Z.: A flexible new technique for camera calibration. PAMI 22 (2000)Google Scholar
  26. 26.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60, 91–110 (2004)CrossRefGoogle Scholar
  27. 27.
    Haralick, R.M., Lee, C.N., Ottenberg, K., Nölle, M.: Review and analysis of solutions of the three point perspective pose estimation problem. IJCV 13 (1994)Google Scholar
  28. 28.
    Reinhard, E., Adhikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. In: Computer Graphics and Applications, vol. 21, pp. 34–41. IEEE, Los Alamitos (2001)Google Scholar
  29. 29.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. PAMI 23, 1222–1239 (2001)CrossRefGoogle Scholar
  30. 30.
    Kolmogorov, V., Zabin, R.: What energy functions can be minimized via graph cuts? PAMI 26, 147–159 (2004)CrossRefGoogle Scholar
  31. 31.
    Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision. PAMI 26, 1124–1137 (2004)CrossRefzbMATHGoogle Scholar
  32. 32.
    Lorensen, W.E., Cline, H.E.: Marching cubes: A high resolution 3d surface construction algorithm. SIGGRAPH 21, 163–169 (1987)CrossRefGoogle Scholar
  33. 33.
    Kim, S., Frahm, J., Pollefeys, M.: Radiometric calibration with illumination change for outdoor scene analysis. In: CVPR, pp. 1–8 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Aparna Taneja
    • 1
  • Luca Ballan
    • 1
  • Marc Pollefeys
    • 1
  1. 1.ETH ZurichSwitzerland

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