Advertisement

Surface Shape from Specularities

  • Jan Erik Solem
  • Anders Heyden
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2749)

Abstract

In this paper a method for reconstructing a specular surface from an image sequence is presented. It is based on tracking both feature points and specularities through the sequence. The feature points are used to calculate the motion of the camera and provide depth information for some points on the boundary of the surface. The specularities give constraints on the surface normal and these constraints are complemented by a smoothness condition in order to estimate the surface shape. The smoothness measure is based on minimizing the surface curvature and a spline representation of the surface is used in the optimization step.

Using the proposed method it is possible to reconstruct completely textureless smooth surfaces. This is shown in both simulated and real experiments.

Keywords

Specularities Surface Estimation Structrure from Motion Splines 

References

  1. 1.
    Pollefeys, M., Koch, R., Van Gool, L.: Self-calibration and metric reconstruction in spite of varying and unknown internal camera parameters. In: Int. Conf. Computer Vision, Mumbai, India (1998) 90–95Google Scholar
  2. 2.
    Nister, D.: Automatic Dense Reconstruction from Uncalibrated Video Sequences. PhD thesis, Royal Institute of Technology, KTH (2001)Google Scholar
  3. 3.
    Hartley, R., Zisserman, A.: Multiple View Geometry. Cambridge University Press, The Edinburgh Building, Cambridge CB2 2RU, UK (2000)zbMATHGoogle Scholar
  4. 4.
    Morris, D., Kanade, T.: Image-consistent surface triangulation. In: Conf. Computer Vision and Pattern Recognition. Volume 1., Hilton Head SC, USA (2000) 332–338Google Scholar
  5. 5.
    Zisserman, A., Giblin, P., Blake, A.: The information available to a moving observer from specularities. Image and vision computing (1989)Google Scholar
  6. 6.
    Zheng, J.Y., Murata, A.: Acuiring a complete 3d model from specular motion under the illumination of circular-shaped light sources. IEEE Trans. Pattern Analysis and Machine Intelligence 22 (2000) 913–920CrossRefGoogle Scholar
  7. 7.
    Savarese, S., Perona, P.: Local analysis for 3d reconstruction of specular surfaces — part ii. In: Proc. European Conf. on Computer Vision. (2002) 759–774Google Scholar
  8. 8.
    Halstead, M.A., Barsky, B.A., Klein, S.A., Mandell, R.B.: Reconstructing curved surfaces from specular reflection patterns using spline fitting of normals. In: Siggraph. (1996) 335–342Google Scholar
  9. 9.
    Heyden, A., Åström, K.: Euclidean reconstruction from image sequences with varying and unknown focal length and principal point. In: Proc. Conf. Computer Vision and Pattern Recognition. (1997) 438–443Google Scholar
  10. 10.
    Triggs, B., McLauchlan, P.F., Hartley, R.I., Fitzgibbon, A.W.: Special sessions — bundle adjustment — a modern synthesis. Lecture Notes in Computer Science 1883 (2000) 298–372Google Scholar
  11. 11.
    Laurentini, A.: The visual hull concept for silhouette-based image understanding. IEEE Trans. Pattern Analysis and Machine Intelligence 16 (1994) 913–920CrossRefGoogle Scholar
  12. 12.
    Farin, G.: Curves and Surfaces for CAGD: A Practical Guide. Academic Press (2002)Google Scholar
  13. 13.
    Pressley, A.: Elementary Differential Geometry. Springer-Verlag (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Jan Erik Solem
    • 1
  • Anders Heyden
    • 1
  1. 1.School of Technology and SocietyMalmö UniversitySweden

Personalised recommendations