ECCV 2010: Computer Vision – ECCV 2010 pp 596-609 | Cite as

Estimation of 3D Object Structure, Motion and Rotation Based on 4D Affine Optical Flow Using a Multi-camera Array

  • Tobias Schuchert
  • Hanno Scharr
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6314)

Abstract

In this paper we extend a standard affine optical flow model to 4D and present how affine parameters can be used for estimation of 3D object structure, 3D motion and rotation using a 1D camera grid. Local changes of the projected motion vector field are modelled not only on the image plane as usual for affine optical flow, but also in camera displacement direction, and in time. We identify all parameters of this 4D fully affine model with terms depending on scene structure, scene motion, and camera displacement. We model the scene by planar, translating, and rotating surface patches and project them with a pinhole camera grid model. Imaged intensities of the projected surface points are then modelled by a brightness change model handling illumination changes. Experiments demonstrate the accuracy of the new model. It outperforms not only 2D affine optical flow models but range flow for varying illumination. Moreover we are able to estimate surface normals and rotation parameters. Experiments on real data of a plant physiology experiment confirm the applicability of our model.

Keywords

Motion Estimate Surface Patch Rotational Model Translational Model Total Little Square 
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.
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Biskup, B., Küsters, R., Scharr, H., Walter, A., Rascher, U.: Quantification of plant surface structures from small baseline stereo images to measure the three-dimensional surface from the leaf to the canopy scale. In: Nova Acta Leopoldina, vol. 357(96), pp. 31–47 (2009)Google Scholar
  2. 2.
    Walter, A., Christ, M.M., Barron-Gafford, G.A., Grieve, K.A., Paige, T., Murthy, R., Rascher, U.: The effect of elevated co2 on diel leaf growth cycle, leaf carbohydrate content and canopy growth performance of populus deltoides. Global Change Biology 8, 1207–1219 (2005)CrossRefGoogle Scholar
  3. 3.
    Schuchert, T., Scharr, H.: Simultaneous estimation of surface motion, depth and slopes under changing illumination. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds.) DAGM 2007. LNCS, vol. 4713, pp. 184–193. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Schuchert, T., Scharr, H.: An affine optical flow model for dynamic surface reconstruction. In: Cremers, D., Rosenhahn, B., Yuille, A.L., Schmidt, F.R. (eds.) Dagstuhl Seminar. LNCS, vol. 5604, pp. 70–90. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Longuet-Higgins, H., Prazdny, K.: The interpretation of a moving retinal image. Proceedings of The Royal Society of London B 208, 385–397 (1980)CrossRefGoogle Scholar
  6. 6.
    Kanatani, K.: Structure from motion without correspondence: general principle. In: Proc. Image Understanding Workshop, pp. 10711–10716 (1985)Google Scholar
  7. 7.
    Adiv, G.: Determining 3-d motion and structure from optical flow generated by several moving objects. PAMI 7, 384–401 (1985)Google Scholar
  8. 8.
    Subbarao, M., Waxman, A.: Closed form solutions to image flow equations for planar surfaces in motion. CVGIP: Graphical Models and Image Processing 36, 208–228 (1986)Google Scholar
  9. 9.
    Szeliski, R.: A multi-view approach to motion and stereo. In: CVPR (1999)Google Scholar
  10. 10.
    Vedula, S., Baker, S., Rander, P., Collins, R., Kanade, T.: Three-dimensional scene flow. PAMI 27, 475–480 (2005)Google Scholar
  11. 11.
    Carceroni, R., Kutulakos, K.: Multi-view 3d shape and motion recovery on the spatio-temporal curve manifold. In: ICCV, vol. (1), pp. 520–527 (1999)Google Scholar
  12. 12.
    Wedel, A., Rabe, C., Vaudrey, T., Brox, T., Franke, U., Cremers, D.: Efficient dense scene flow from sparse or dense stereo data. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 739–751. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
    Pons, J.P., Keriven, R., Faugeras, O.: Multi-view stereo reconstruction and scene flow estimation with a global image-based matching score. IJCV 72(2), 179–193 (2007)CrossRefGoogle Scholar
  14. 14.
    Kolev, K., Klodt, M., Brox, T., Cremers, D.: Continuous global optimization in multiview 3d reconstruction. IJCV 84, 80–96 (2009)CrossRefGoogle Scholar
  15. 15.
    Fleet, D., Weiss, Y.: Optical flow estimation. In: Mathematical models for Computer Vision: The Handbook. Springer, Heidelberg (2005)Google Scholar
  16. 16.
    Matthies, L.H., Szeliski, R., Kanade, T.: Kalman filter-based algorithms for estimating depth from image sequences. IJCV 3, 209–236 (1989)CrossRefGoogle Scholar
  17. 17.
    Cason, C.: Persistence of vision ray tracer (POV-Ray), version 3.6, Windows (2005)Google Scholar
  18. 18.
    Spies, H., Jähne, B., Barron, J.: Range flow estimation. CVIU 85, 209–231 (2002)MATHGoogle Scholar
  19. 19.
    Scharr, H.: Optimal filters for extended optical flow. In: Jähne, B., Mester, R., Barth, E., Scharr, H. (eds.) IWCM 2004. LNCS, vol. 3417, pp. 14–29. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  20. 20.
    Barron, J., Fleet, D., Beauchemin, S.: Performance of optical flow techniques. IJCV 12(1), 43–77 (1994)CrossRefGoogle Scholar
  21. 21.
    Schuchert, T., Aach, T., Scharr, H.: Range flow for varying illumination. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 509–522. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  22. 22.

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Tobias Schuchert
    • 1
    • 2
  • Hanno Scharr
    • 2
  1. 1.Fraunhofer Institute of OptronicsSystem Technologies and Image ExploitationKarlsruheGermany
  2. 2.Institute for Chemistry and Dynamics of the GeosphereICG-3: Phytosphere, ForschungszentrumJülichGermany

Personalised recommendations