Fast Random Sample Matching of 3d Fragments

  • Simon Winkelbach
  • Markus Rilk
  • Christoph Schönfelder
  • Friedrich M. Wahl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3175)


This paper proposes an efficient pairwise surface matching approach for the automatic assembly of 3d fragments or industrial components. The method rapidly scans through the space of all possible solutions by a special kind of random sample consensus (RANSAC) scheme. By using surface normals and optionally simple features like surface curvatures, we can highly constrain the initial 6 degrees of freedom search space of all relative transformations between two fragments. The suggested approach is robust, very time and memory efficient, easy to implement and applicable to all kinds of surface data where surface normals are available (e.g. range images, polygonal object representations, point clouds with neighbor connectivity, etc.).


Range Image Iterative Close Point Point Pair Iterative Close Point Matching Quality 
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.


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  1. 1.
    Besl, P., McKay, N.: A method for registration of 3-D shapes. IEEE Trans. PAMI 14(2), 239–256 (1992)Google Scholar
  2. 2.
    Horn, B.K.P.: Closed-form solution of absolute orientation using unit quaternions, J. Opt. Soc. Amer. A 4(4), 629–642 (1987)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Chua, C.S., Jarvis, R.: Point Signatures: A New Representation for 3D Object Recognition. Int’l Journal of Computer Vision 25(1), 63–85 (1997)CrossRefGoogle Scholar
  4. 4.
    Papaioannou, G., Theoharis, T.: Fast Fragment Assemblage Using Boundary Line and Surface Matching. In: IEEE Proc. ICPR/ACVA (2003)Google Scholar
  5. 5.
    Johnson, A.E., Hebert, M.: Surface registration by matching oriented points. In: Proc. lnt’l. Conf. Recent Advances in 3-D Digital Imaging and Modeling (3DIM) (1997)Google Scholar
  6. 6.
    Stockman, G.: Object Recognition and Localization via Pose Clustering. Computer Vision, Graphics, and Image Processing 40, 361–387 (1987)CrossRefGoogle Scholar
  7. 7.
    Linnainmaa, S., Harwood, D., Davis, L.S.: Pose determination of a threedimensional object using triangle pairs. IEEE Trans. PAMI 10(5), 634–647 (1988)Google Scholar
  8. 8.
    Barequet, G., Sharir, M.: Partial Surface Matching by Using Directed Footprints. In: Computational Geometry 1996, Philadelphia PA, USA (1996)Google Scholar
  9. 9.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with application to image analysis and automated cartography. Communication of the ACM 24(6), 381–395 (1981)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Chen, C.S., Hung, Y.P., Cheng, J.B.: RANSAC-Based DARCES: A New Approach to Fast Automatic Registration of Partially Overlapping Range Images. IEEE Trans. PAMI 21(11), 1229–1234 (1999)Google Scholar
  11. 11.
    Leitão, H.C.G., Stolfi, J.: A Multiscale Method for the Reassembly of Two- Dimensional Fragmented Objects. IEEE Trans. PAMI 24(9), 1239–1251 (2002)Google Scholar
  12. 12.
    Papaioannou, G., Karabassi, E.A., Theoharis, T.: Reconstruction of Threedimensional Objects through Matching of their Parts. IEEE Trans. PAMI 24(1), 114–124 (2002)Google Scholar
  13. 13.
    Desbrun, M., Meyer, M., Schröder, P., Barr, A.H.: Implicit Fairing of Irregular Meshes using Diffusion and Curvature Flow. In: Proc. SIGGRAPH 1999, pp. 317–324 (1999)Google Scholar
  14. 14.
    Johnson, A.E., Hebert, M.: Control of Polygonal Mesh Resolution for 3-D Computer Vision. Tech. Report CMU-RI-TR-96-20, Robotics Institute, Carnegie Mellon University (April 1997)Google Scholar
  15. 15.
    Friedman, J.H., Bentley, J.L., Finkel, R.A.: An Algorithm for Finding Best Matches in Logarithmic Expected Time. ACM Trans. Mathematical Software 3(3), 209–226 (1977)zbMATHCrossRefGoogle Scholar
  16. 16.
    Winkelbach, S., Westphal, R., Goesling, T.: Pose Estimation of Cylindrical Fragments for Semi-Automatic Bone Fracture Reduction. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 566–573. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Simon Winkelbach
    • 1
  • Markus Rilk
    • 1
  • Christoph Schönfelder
    • 1
  • Friedrich M. Wahl
    • 1
  1. 1.Institute for Robotics and Process ControlTechnical University of BraunschweigBraunschweigGermany

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