3D Reconstruction of Metallic Surfaces by Photopolarimetric Analysis

  • P. d’Angelo
  • C. Wöhler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3540)

Abstract

In this paper we present a novel image-based 3D surface reconstruction technique that incorporates both reflectance and polarisation features into a variational framework. Our technique is especially suited for the difficult task of 3D reconstruction of rough metallic surfaces. An error functional consisting of several error terms related to the measured reflectance and polarisation properties is minimised in order to obtain a 3D reconstruction of the surface. We show that the combined approach strongly increases the accuracy of the surface reconstruction result, compared to techniques based on either reflectance or polarisation alone. We evaluate the algorithm based on synthetic ground truth data. Furthermore, we report 3D reconstruction results for a raw forged iron surface, thus showing the applicability of our method in real-world scenarios, here in the domain of quality inspection in the automotive industry.

Keywords

Polarisation Angle Metallic Surface Polarisation Feature Polarisation Property Polarisation Degree 
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.

References

  1. 1.
    Batlle, J., Mouaddib, E., Salvi, J.: Recent progress in coded structured light as a technique to solve the correspondence problem: a survey. Pattern Recognition 31(7), 963–982 (1998)CrossRefGoogle Scholar
  2. 2.
    Horn, B.K.P., Brooks, M.J.: Shape from Shading. MIT Press, Cambridge (1989)Google Scholar
  3. 3.
    Horn, B.K.P.: Height and Gradient from Shading. MIT technical report 1105A, http://people.csail.mit.edu/people/bkph/AIM/AIM-1105A-TEX.pdf
  4. 4.
    Jiang, X., Bunke, H.: Dreidimensionales Computersehen. Springer, Berlin (1997)Google Scholar
  5. 5.
    Miyazaki, D., Kagesawa, M., Ikeuchi, K.: Transparent Surface Modeling from a Pair of Polarization Images. IEEE Trans. on Pattern Analysis and Machine Intelligence 26(1), 73–82 (2004)CrossRefGoogle Scholar
  6. 6.
    Nayar, S.K., Ikeuchi, K., Kanade, T.: Surface Reflection: Physical and Geometrical Perspectives. IEEE Trans. on Pattern Analysis and Machine Intelligence 13(7), 611–634 (1991)CrossRefGoogle Scholar
  7. 7.
    Rahmann, S., Canterakis, N.: Reconstruction of Specular Surfaces using Polarization Imaging. In: Int. Conf. on Computer Vision and Pattern Recogntion, Kauai, USA, vol. I, pp. 149–155 (2001)Google Scholar
  8. 8.
    Wöhler, C., Hafezi, K.: A general framework for three-dimensional surface reconstruction by self-consistent fusion of shading and shadow features. Pattern Recognition 38(7), 965–983 (2005)CrossRefGoogle Scholar
  9. 9.
    Wolff, L.B.: Constraining Object Features Using a Polarization Reflectance Model. IEEE Trans. on Pattern Analysis and Machine Intelligence 13(7), 635–657 (1991)CrossRefGoogle Scholar
  10. 10.
    Wolff, L.B.: Polarization vision: a new sensory approach to image understanding. Image and Vision Computing 15, 81–93 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • P. d’Angelo
    • 1
  • C. Wöhler
    • 1
  1. 1.Machine PerceptionDaimlerChrysler Research and TechnologyUlmGermany

Personalised recommendations