Depth Auto-calibration for Range Cameras Based on 3D Geometry Reconstruction

  • Benjamin Langmann
  • Klaus Hartmann
  • Otmar Loffeld
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7432)


An approach for auto-calibration and validation of depth measurements gained from range cameras is introduced. Firstly, the geometry of the scene is reconstructed and its surface normals are computed. These normal vectors are segmented in 3D with the Mean-Shift algorithm and large planes like walls or the ground plane are recovered. The 3D reconstruction of the scene geometry is then utilized in a novel approach to derive principal camera parameters for range or depth cameras. It operates based on a single range image alone and does not require special equipment such as markers or a checkerboard and no specific measurement procedures as are necessary for previous methods. The fact that wrong camera parameters deform the geometry of the objects in the scene is utilized to infer the constant depth error (the phase offset for continuous wave ToF cameras) as well as the focal length. The proposed method is applied to ToF cameras which are based on the Photonic Mixer Device to measure the depth of objects in the scene. Its capabilities as well as its current and systematic limitations are addressed and demonstrated.


Focal Length Camera Parameter Real World Scene Calibration Object Orthogonality Measure 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Möller, T., Kraft, H., Frey, J., Albrecht, M., Lange, R.: Robust 3d measurement with pmd sensors. Range Imaging Day, Zürich (2005)Google Scholar
  2. 2.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press (2004) ISBN: 0521540518Google Scholar
  3. 3.
    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
  4. 4.
    Lindner, M., Schiller, I., Kolb, A., Koch, R.: Time-of-flight sensor calibration for accurate range sensing. Computer Vision and Image Understanding 114, 1318–1328 (2010)CrossRefGoogle Scholar
  5. 5.
    Schiller, I., Beder, C.: Calibration of a pmd-camera using a planar calibration pattern together with a multi-camera setup. In: Proc. XXXVII Int. Soc. for Photogrammetry (2008)Google Scholar
  6. 6.
    Falie, D., Buzuloiu, V.: Further investigations on tof cameras distance errors and their corrections. In: 4th European Conference on Circuits and Systems for Communications, pp. 197–200 (2008)Google Scholar
  7. 7.
    Kahlmann, T., Remondino, F., Ingensand, H.: Calibration for increased accuracy of the range imaging camera swissrangertm. Image Engineering and Vision Metrology (IEVM) 36, 136–141 (2006)Google Scholar
  8. 8.
    Radmer, J., Fuste, P.M., Schmidt, H., Kruger, J.: Incident light related distance error study and calibration of the pmd-range imaging camera. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 1–6 (2008)Google Scholar
  9. 9.
    Kim, Y., Chan, D., Theobalt, C., Thrun, S.: Design and calibration of a multi-view tof sensor fusion system. In: IEEE Conf. on Computer Vision & Pattern Recogn.; Workshop on ToF-Camera based Computer Vision, pp. 1–7. IEEE (2008)Google Scholar
  10. 10.
    Prasad, T., Hartmann, K., Weihs, W., Ghobadi, S.E., Sluiter, A.: First steps in enhancing 3d vision technique using 2d/3d sensors. In: Computer Vision Winter Workshop, Telc, Czech Republic, Citeseer, pp. 82–86 (2006)Google Scholar
  11. 11.
    Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 603–619 (2002)CrossRefGoogle Scholar
  12. 12.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Benjamin Langmann
    • 1
  • Klaus Hartmann
    • 1
  • Otmar Loffeld
    • 1
  1. 1.ZESS - Center for Sensor SystemsUniversity of SiegenSiegenGermany

Personalised recommendations