Skip to main content

Automatic Range Image Registration Using Mixed Integer Linear Programming

  • Conference paper
Computer Vision – ACCV 2007 (ACCV 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4844))

Included in the following conference series:

Abstract

A coarse registration method using Mixed Integer Linear Programming (MILP) is described that finds global optimal registration parameter values that are independent of the values of invariant features. We formulate the range image registration problem using MILP. Our algorithm using MILP formulation finds the best balanced optimal registration for robustly aligning two range images with the best balanced accuracy. It adjusts the error tolerance automatically in accordance with the accuracy of the given range image data. Experimental results show that this method of coarse registration is highly effective.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Besl, P.J., McKay, N.D.: A Method for Registration of 3-D Shapes. IEEE Trans. on PAMI 14(2), 239–256 (1992)

    Google Scholar 

  2. Campbell, R.J., Flynn, P.J.: A Survey of Free-Form Object Representation and Recognition Techniques. CVIU 81, 166–210 (2001)

    MATH  Google Scholar 

  3. Chen, C.C., Stamos, I.: Range Image Registration Based on Circular Features. In: Proc. 3DPVT, pp. 447–454 (2006)

    Google Scholar 

  4. Chua, C.S., Jarvis, R.: 3D Free-Form Surface Registration and Object Recognition. IJCV 17(1), 77–99 (1996)

    Article  Google Scholar 

  5. He, W., Ma, W., Zha, H.: Automatic Registration of Range Images Based on Correspondence of Complete Plane Patches. In: Proc. 3DIM, pp. 470–475 (2005)

    Google Scholar 

  6. Higuchi, K., Hebert, M., Ikeuchi, K.: Building 3-D Models from Unregistered Range Images. GMIP 57(4), 315–333 (1995)

    Google Scholar 

  7. Johnson, A.E., Hebert, M.: Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes. IEEE Trans. on PAMI 21(5), 433–449 (1999)

    Google Scholar 

  8. Johnson, E.L., Nemhauser, G.L., Savelsbergh, M.W.P.: Progress in Linear Programming-Based Algorithms for Integer Programming: An Exposition. INFORMS Journal on Computing 12(1), 2–23 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  9. Koenderink, J.J.: Solid Shape. MIT Press, Cambridge (1990)

    Google Scholar 

  10. Šára, R., Okatahi, I.S., Sugimoto, A.: Globally Convergent Range Image Registration by Graph Kernel Algorithm. In: Proc. 3DIM, pp. 377–384 (2005)

    Google Scholar 

  11. Rusinkiewicz, S., Levoy, M.: Efficient Variants of the ICP Algorithm. In: Proc. 3DIM, pp. 145–152 (2001)

    Google Scholar 

  12. Stein, F., Medioni, G.: Structural indexing: Efficient 3-D object recognition. IEEE Trans. on PAMI 14(2), 125–145 (1992)

    Google Scholar 

  13. Umeyama, S.: Least-Square Estimation of Transformation Parameters Between Two Point Patterns. IEEE Trans. on PAMI 13(4), 376–380 (1991)

    Google Scholar 

  14. Stanford 3D Scanning Repository, http://www-graphics.stanford.edu/data/3Dscanrep/

  15. The Ohio State University Range Image Repository, http://sampl.ece.ohio-state.edu/data/3DDB/RID/minolta/

  16. Georgia Institute of Technology Large Geometric Models Archive, http://www-static.cc.gatech.edu/projects/large_models/

Download references

Author information

Authors and Affiliations

Authors

Editor information

Yasushi Yagi Sing Bing Kang In So Kweon Hongbin Zha

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Sakakubara, S., Kounoike, Y., Shinano, Y., Shimizu, I. (2007). Automatic Range Image Registration Using Mixed Integer Linear Programming. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4844. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76390-1_42

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-76390-1_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76389-5

  • Online ISBN: 978-3-540-76390-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics