A Method for Asteroids 3D Surface Reconstruction from Close Approach Distances

  • Luca Baglivo
  • Alessio Del Bue
  • Massimo Lunardelli
  • Francesco Setti
  • Vittorio Murino
  • Mariolino De Cecco
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6962)

Abstract

We present a procedure for asteroids 3D surface reconstruction from images for close approach distances. Different from other 3D reconstruction scenario from spacecraft images, the closer flyby gave the chance to revolve around the asteroid shape and thus acquiring images from different viewpoints with a higher baseline. The chance to have more information of the asteroids surface is however paid by the loss of correspondences between images given the larger baseline. In this paper we present a procedure used to reconstruct the 3D surface of the asteroid 21 Lutetia encountered by Rosetta spacecraft on July the 10 th of 2010 at the closest approach distance of 3170 Km. It was possible to reconstruct a wider surface even dealing with strong ratio of missing data in the measurements. Results show the reconstructed 3D surface of the asteroid as a sparse 3D mesh.

Keywords

Astronomy Structure from Motion Asteroid 3D reconstruction 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Speeded-up robust features (SURF). Computer Vision and Image Understanding 110(3), 346–359 (2008); similarity Matching in Computer Vision and MultimediaCrossRefGoogle Scholar
  2. 2.
    Carry, B., et al.: Physical properties of the ESA Rosetta target asteroid (21) Lutetia. II. Shape and flyby geometry. Astronomy and Astrophysics 523, A94+ (2010)Google Scholar
  3. 3.
    Carry, B., et al.: The KOALA Shape Modeling Technique Validated at (21) Lutetia by ESA Rosetta Mission. Bulletin of the American Astronomical Society, 1050–+ (2010)Google Scholar
  4. 4.
    Christy, S., Horaud, R.: Euclidean shape and motion from multiple perspective views by affine iterations. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(11), 1098–1104 (1996)CrossRefGoogle Scholar
  5. 5.
    De Cecco, M., Pertile, M., Baglivo, L., Lunardelli, M.: A unified framework for uncertainty, compatibility analysis, and data fusion for multi-stereo 3-d shape estimation. IEEE Trans. Instr. Meas. 59(11), 2834–2842 (2010)CrossRefGoogle Scholar
  6. 6.
    Del Bue, A., Xavier, J., Agapito, L., Paladini, M.: Bilinear factorization via augmented lagrange multipliers. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 283–296. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Fischler, M., Bolles, R.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24(6), 381–395 (1981)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2000); ISBN: 0521623049MATHGoogle Scholar
  9. 9.
    Keller, H.U., et al.: OSIRIS The Scientific Camera System Onboard Rosetta. Space Science Review 128, 433–506 (2007)CrossRefGoogle Scholar
  10. 10.
    Kochemasov, G.G.: Waiting for 21-Lutetia ”Rosetta” images as a final proof of structurizing force of inertia-gravity waves. In: EGU General Assembly 2010, Vienna, Austria, May 2-7, p. 4070 (May 2010)Google Scholar
  11. 11.
    Kovesi, P.D.: MATLAB and Octave functions for computer vision and image processing. Centre for Exploration Targeting, School of Earth and Environment, The University of Western Australia, http://www.csse.uwa.edu.au/~pk/research/matlabfns/
  12. 12.
    Lamy, P.L., Faury, G., Jorda, L., Kaasalainen, M., Hviid, S.F.: Multi-color, rotationally resolved photometry of asteroid 21 Lutetia from OSIRIS/Rosetta observations. Astronomy ad Astrophysics  521, A19+ (2010)Google Scholar
  13. 13.
    Marques, M., Costeira, J.P.: Estimating 3d shape from degenerate sequences with missing data. Computer Vision and Image Understanding 113(2), 261–272 (2009)CrossRefGoogle Scholar
  14. 14.
    Schweighofer, G., Pinz, A.: Globally Optimal O(n) Solution to the PnP Problem for General Camera Models. In: Proc. British Machine Vision Conference, pp. 1–8 (2008)Google Scholar
  15. 15.
    Tomasi, C., Kanade, T.: Shape and motion from image streams under orthography: A factorization approach. International Journal of Computer Vision 9(2) (1992)Google Scholar
  16. 16.
    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, pp. 298–375. Springer, Heidelberg (2000), citeseer.nj.nec.com/triggs00bundle.html CrossRefGoogle Scholar
  17. 17.
    Zeisl, B., Georgel, P., Schweiger, F., Steinbach, E., Navab, N.: Estimation of location uncertainty for scale invariant feature points. In: British Machine Vision Conference, BMVC (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Luca Baglivo
    • 1
  • Alessio Del Bue
    • 2
  • Massimo Lunardelli
    • 1
  • Francesco Setti
    • 1
  • Vittorio Murino
    • 2
    • 3
  • Mariolino De Cecco
    • 1
  1. 1.Department of Mechanical and Structural EngineeringUniversity of TrentoTrentoItaly
  2. 2.Istituto Italiano di Tecnologia (IIT)GenovaItaly
  3. 3.Department of Computer ScienceUniversity of VeronaVeronaItaly

Personalised recommendations