Bundle Adjustment for Stereoscopic 3D

  • Christian Kurz
  • Thorsten Thormählen
  • Hans-Peter Seidel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6930)


The recent resurgence of stereoscopic 3D films has triggered a high demand for post-processing tools for stereoscopic image sequences. Camera motion estimation, also known as structure-from-motion (SfM) or match-moving, is an essential step in the post-processing pipeline. In order to ensure a high accuracy of the estimated camera parameters, a bundle adjustment algorithm should be employed. We present a new stereo camera model for bundle adjustment. It is designed to be applicable to a wide range of cameras employed in today’s movie productions. In addition, we describe how the model can be integrated efficiently into the sparse bundle adjustment framework, enabling the processing of stereoscopic image sequences with traditional efficiency and improved accuracy. Our camera model is validated by synthetic experiments, on rendered sequences, and on a variety of real-world video sequences.


Camera Model Stereo Camera Bundle Adjustment Visual Odometry Left Camera 
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.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2003)zbMATHGoogle Scholar
  2. 2.
    Triggs, B., McLauchlan, P.F., Hartley, R.I., Fitzgibbon, A.W.: Bundle adjustment – A modern synthesis. In: Triggs, B., Zisserman, A., Szeliski, R. (eds.) ICCV-WS 1999. LNCS, vol. 1883, p. 298. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  3. 3.
    Hasler, N., Rosenhahn, B., Thormählen, T., Wand, M., Gall, J., Seidel, H.-P.: Markerless motion capture with unsynchronized moving cameras. In: CVPR (2009)Google Scholar
  4. 4.
    Goesele, M., Snavely, N., Curless, B., Hoppe, H., Seitz, S.M.: Multi-view stereo for community photo collections. In: Intl. Conference on Computer Vision (2007)Google Scholar
  5. 5.
    Kim, J.H., Li, H., Hartley, R.: Motion estimation for multi-camera systems using global optimization. In: Computer Vision and Pattern Recognition (2008)Google Scholar
  6. 6.
    Stewenius, H., Åström, K.: Structure and motion problems for multiple rigidly moving cameras. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3023, pp. 252–263. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Frahm, J.-M., Köser, K., Koch, R.: Pose estimation for multi-camera systems. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds.) DAGM 2004. LNCS, vol. 3175, pp. 286–293. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. 8.
    Chandraker, M., Lim, J., Kriegman, D.J.: Moving in stereo: Efficient structure and motion using lines. In: International Conference on Computer Vision (2009)Google Scholar
  9. 9.
    Hirschmüller, H., Innocent, P.R., Garibaldi, J.M.: Fast, unconstrained camera motion estimation from stereo without tracking and robust statistics. In: International Conference on Control, Automation, Robotics and Vision (2002)Google Scholar
  10. 10.
    Di, K., Xu, F., Li, R.: Constrained bundle adjustment of panoramic stereo images for mars landing site mapping. In: Mobile Mapping Technology (2004)Google Scholar
  11. 11.
    Zhang, Z., Luong, Q.T., Faugeras, O.: Motion of an uncalibrated stereo rig: Self-calibration and metric reconstruction. TRA 12, 103–113 (1996)Google Scholar
  12. 12.
    Brooks, M.J., de Agapito, L., Huynh, D.Q., Baumela, L.: Towards robust metric reconstruction via a dynamic uncalibrated stereo head. Image and Vision Computing 16, 989–1002 (1998)CrossRefGoogle Scholar
  13. 13.
    Matthies, L., Shafer, S.A.: Error modeling in stereo navigation. IEEE Journal of Robotics and Automation 3, 239–250 (1987)CrossRefGoogle Scholar
  14. 14.
    Molton, N., Brady, M.: Practical structure and motion from stereo when motion is unconstrained. International Journal of Computer Vision 39, 5–23 (2000)CrossRefzbMATHGoogle Scholar
  15. 15.
    Saeedi, P., Lawrence, P.D., Lowe, D.G.: 3d motion tracking of a mobile robot in a natural environment. In: ICRA, pp. 1682–1687 (2000)Google Scholar
  16. 16.
    Weng, J., Cohen, P., Rebibo, N.: Motion and structure estimation from stereo image sequences. Transactions on Robotics and Automation 8, 362–382 (1992)CrossRefGoogle Scholar
  17. 17.
    Olson, C.F., Matthies, L.H., Schoppers, M., Maimone, M.W.: Rover navigation using stereo ego-motion. Robotics and Autonomous Systems 43, 215–229 (2003)CrossRefGoogle Scholar
  18. 18.
    Sünderhauf, N., Konolige, K., Lacroix, S., Protzel, P.: Visual odometry using sparse bundle adjustment on an autonomous outdoor vehicle. In: AMS (2005)Google Scholar
  19. 19.
    Sünderhauf, N., Protzel, P.: Towards using sparse bundle adjustment for robust stereo odometry in outdoor terrain. In: TAROS, pp. 206–213 (2006)Google Scholar
  20. 20.
    Nister, D., Naroditsky, O., Bergen, J.: Visual odometry. In: CVPR (2004)Google Scholar
  21. 21.
    Dang, T., Hoffmann, C., Stiller, C.: Continuous stereo self-calibration by camera parameter tracking. Transactions on Image Processing 18, 1536–1550 (2009)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Mandelbaum, R., Salgian, G., Sawhney, H.: Correlation-based estimation of ego-motion and structure from motion and stereo. In: ICCV, vol. 1, pp. 544–550 (1999)Google Scholar
  23. 23.
    Akhloufi, M., Polotski, V., Cohen, P.: Virtual view synthesis from uncalibrated stereo cameras. In: Multimedia Computing and Systems, pp. 672–677 (1999)Google Scholar
  24. 24.
    Hartley, R., Gupta, R., Chang, T.: Stereo from uncalibrated cameras. In: IEEE Conference on Computer Vision and Pattern Recognition (1992)Google Scholar
  25. 25.
    Ko, J.H., Park, C.J., Kim, E.S.: A new rectification scheme for uncalibrated stereo image pairs and its application to intermediate view reconstruction. In: Optical Information Systems II, Proceedings of SPIE, vol. 5557, pp. 98–109 (2004)Google Scholar
  26. 26.
    Yin, X., Xie, M.: Estimation of the fundamental matrix from uncalibrated stereo hand images for 3d hand gesture recognition. PR 36, 567–584 (2003)Google Scholar
  27. 27.
    Zhang, Z., Xu, G.: A unified theory of uncalibrated stereo for both perspective and affine cameras. Journal of Mathematical Imaging and Vision 9, 213–229 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Hodges, S., Richards, R.: Uncalibrated stereo vision for pcb drilling. In: IEEE Colloquium on Application of Machine Vision (1995)Google Scholar
  29. 29.
    Park, J.S., Chung, M.J.: Path planning with uncalibrated stereo rig for image-based visual servoing under large pose discrepancy. TRA 19, 250–258 (2003)Google Scholar
  30. 30.
    Shimizu, Y., Sato, J.: Visual navigation of uncalibrated mobile robots from uncalibrated stereo pointers. In: Intl. Conf. on Pattern Recognition, pp. 346–349 (2000)Google Scholar
  31. 31.
    Cipolla, R., Hadfield, P.A., Hollinghurst, N.J.: Uncalibrated stereo vision with pointing for a man-machine interface. In: MVA, pp. 163–166 (1994)Google Scholar
  32. 32.
    Simond, N., Rives, P.: Trajectography of an uncalibrated stereo rig in urban environments. Intelligent Robots and Systems 4, 3381–3386 (2004)Google Scholar
  33. 33.
    Fusiello, A., Irsara, L.: Quasi-euclidean uncalibrated epipolar rectification. In: IEEE International Conference on Pattern Recognition (2008)Google Scholar
  34. 34.
    Bleyer, M., Gelautz, M.: Temporally consistent disparity maps from uncalibrated stereo videos. In: Image and Signal Processing and Analysis (2009)Google Scholar
  35. 35.
    Cheng, C.M., Lai, S.H., Su, S.H.: Self image rectification for uncalibrated stereo video with varying camera motions and zooming effects. In: MVA, pp. 21–24 (2009)Google Scholar
  36. 36.
    Huguet, F., Devernay, F.: A variational method for scene flow estimation from stereo sequences. In: International Conference on Computer Vision (2007)Google Scholar
  37. 37.
    Min, D., Sohn, K.: Edge-preserving simultaneous joint motion-disparity estimation. In: IEEE International Conference on Pattern Recognition, pp. 74–77 (2006)Google Scholar
  38. 38.
    Zhang, Y., Kambhamettu, C.: On 3-D scene flow and structure recovery from multiview image sequences. Systems, Man, and Cybernetics 33, 592–606 (2003)CrossRefGoogle Scholar
  39. 39.
    Vedula, S., Baker, S., Rander, P., Collins, R.T., Kanade, T.: Three-dimensional scene flow. In: International Conference on Computer Vision, pp. 722–729 (1999)Google Scholar
  40. 40.
    Trinh, H., McAllester, D.: Structure and motion from road-driving stereo sequences. In: 3D Information Extraction for Video Analysis and Mining (2009)Google Scholar
  41. 41.
    Pollefeys, M., Gool, L.V., Vergauwen, M., Cornelis, K., Verbiest, F., Tops, J.: Video-to-3D. In: ISPRS Commission V Symposium (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Christian Kurz
    • 1
  • Thorsten Thormählen
    • 1
  • Hans-Peter Seidel
    • 1
  1. 1.Max Planck Institute for Computer Science (MPII)SaarbrückenGermany

Personalised recommendations