Supporting Structure from Motion with a 3D-Range-Camera

  • Birger Streckel
  • Bogumil Bartczak
  • Reinhard Koch
  • Andreas Kolb
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4522)


Tracking of a camera pose in all 6 degrees of freedom is a task with many applications in 3D-imaging as i.e. augmentation or robot navigation. Structure from motion is a well known approach for this task, with several well known restrictions. These are namely the scale ambiguity of the calculated relative pose and the need of a certain camera movement (preferably lateral) to initiate the tracking.

In the last few years time-of-flight imaging sensors were developed that allow the measuring of metric depth over a whole region with a frame rate similar to a standard CCD-camera.

In this work a camera rig consisting of a standard 2D CCD camera and a time-of-flight 3D camera is used. Structure from motion is calculated on the 2D image, aided by the depth measurement from the time-of-flight camera to overcome the restrictions named above. It is shown how the additional 3D-information can be used to improve the accuracy of the camera pose estimation.


Depth Image Forward Movement Robot Navigation Rotation Error Structure From Motion 
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.
    Bouguet, J.Y.: Camera calibration toolbox for matlab (1998),
  2. 2.
    Davison, A.J.: Real-time simultaneous localisation and mapping with a single camera. In: Proc. ICCV (2003)Google Scholar
  3. 3.
    Hartley, R., Zissermann, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2004)zbMATHGoogle Scholar
  4. 4.
    Koch, R., Koeser, K., Streckel, B., Evers-Senne, J.-F.: Markerless image-based 3d tracking for real-time augmented reality applications. In: WIAMIS 2005, Montreux, Switzerland (April 2005)Google Scholar
  5. 5.
    Koeser, K., Bartczak, B., Koch, R.: Drift-free pose estimation with hemispherical cameras. In: Proceedings of CVMP 2006, London (November 2006)Google Scholar
  6. 6.
    Lindner, M., Kolb, A.: Lateral and Depth Calibration of PMD-Distance Sensors. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Remagnino, P., Nefian, A., Meenakshisundaram, G., Pascucci, V., Zara, J., Molineros, J., Theisel, H., Malzbender, T. (eds.) ISVC 2006. LNCS, vol. 4292, pp. 524–533. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Kraft, H., Frey, J., Moeller, T., Albrecht, M., Grothof, M., Schink, B., Hess, H., Buxbaum, B.: 3d-camera of high 3d-frame rate, depth-resolution and background light elimination based on improved pmd (photonic mixer device)-technologies. In: 6th Intl Conference for Optical Technologies, Optical Sensors and Measuring Techniques (OPTO 2004), Nuernberg, Germany (May 2004)Google Scholar
  8. 8.
    Kuhnert, K.-D., Stommel, M.: Fusion of stereo-camera and pmd-camera data for real- time suited precise 3d environment reconstruction. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’06), Beijing, China (Oct. 2006)Google Scholar
  9. 9.
    Lange, R., Seitz, P., Biber, A., Schwarte, R.: Time-of-flight range imaging with a custom solid-state imagesensor. In: EOS/SPIE Laser Metrology and Inspection, vol. 3823 (1999)Google Scholar
  10. 10.
    Lepetit, V., Fua, P.: Monocular model-based 3d tracking of rigid objects: A survey. Foundations and Trends in Computer Graphics and Vision 1(1), 1–89 (2005)CrossRefGoogle Scholar
  11. 11.
    Pollefeys, M., van Gool, L., Vergauwen, M., Verbiest, F., Cornelis, K., Tops, J., Koch, R.: Visual modeling with a hand-held camera. International Journal of Computer Vision 59(3), 207–232 (2004)CrossRefGoogle Scholar
  12. 12.
    Prasad, T.D.A., Hartmann, K., Wolfgang, W., Ghobadi, S.E., Sluiter, A.: First steps in enhancing 3d vision technique using 2d/3d sensors. In: Chum, V., Franc, O. (eds.) Computer Vision Winter Workshop 2006, Telc, Czech Republic, pp. 82–86 (2006)Google Scholar
  13. 13.
    Shi, J., Tomasi, C.: Good features to track. In: Conference on Computer Vision and Pattern Recognition, Seattle, June 1994, pp. 593–600. IEEE Computer Society Press, Los Alamitos (1994)Google Scholar
  14. 14.
    Welch, G., Bishop, G.: An introduction to the kalman filter. Technical Report TR 95-041, University of North Carolina, Department of Computer Science (2001)Google Scholar
  15. 15.
    Xu, Z., Schwarte, R., Heinol, H., Buxbaum, B., Ringbeck, T.: Smart pixel - photonic mixer device (pmd). In: M2VIP’98 - International Conference on Mechatronics and Machine Vision in Practice, pp. 259–264 (1998)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Birger Streckel
    • 1
  • Bogumil Bartczak
    • 1
  • Reinhard Koch
    • 1
  • Andreas Kolb
    • 2
  1. 1.Institute of Computer Science, Christian-Albrechts-University of Kiel, 24098 KielGermany
  2. 2.Computer Graphics Group, University of Siegen, 57068 SiegenGermany

Personalised recommendations