Correspondences of Persistent Feature Points on Near-Isometric Surfaces

  • Ying Yang
  • David Günther
  • Stefanie Wuhrer
  • Alan Brunton
  • Ioannis Ivrissimtzis
  • Hans-Peter Seidel
  • Tino Weinkauf
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7583)


We present a full pipeline for finding corresponding points between two surfaces based on conceptually simple and computationally efficient components. Our pipeline begins with robust and stable extraction of feature points from the surfaces. We then find a set of near isometric correspondences between the feature points by solving an optimization problem using established components. The performance is evaluated on a large number of 3D models from the following perspectives: robustness w.r.t. isometric deformation, robustness w.r.t. noise and incomplete surfaces, partial matching, and anisometric deformation.


Feature Point Gaussian Curvature Markov Random Field Partial Match Heat Kernel Signature 
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.
    van Kaick, O., Zhang, H., Hamarneh, G., Cohen-Or, D.: A survey on shape correspondence. CGF 30, 1681–1707 (2011)Google Scholar
  2. 2.
    Bronstein, A., Bronstein, M., Castellani, U., Dubrovina, A., Guibas, L., Horaud, R., Kimmel, R., Knossow, D., von Lavante, E., Mateus, D., Ovsjanikov, M., Sharma, A.: SHREC 2010: robust correspondence benchmark. In: 3DOR (2010)Google Scholar
  3. 3.
    Edelsbrunner, H., Letscher, D., Zomorodian, A.: Topological persistence and simplification. DCG 28, 511–533 (2002)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Sun, J., Ovsjanikov, M., Guibas, L.: A concise and provably informative multi-scale signature based on heat diffusion. In: SGP, pp. 1383–1392 (2009)Google Scholar
  5. 5.
    Dey, T., Li, K., Luo, C., Ranjan, P., Safa, I., Wang, Y.: Persistent heat signature for pose-oblivious matching of incomplete models. CGF 29, 1545–1554 (2010)Google Scholar
  6. 6.
    Zaharescu, A., Boyer, E., Varanasi, K., Horaud, R.: Surface feature detection and description with applications to mesh matching. In: CVPR, pp. 373–380 (2009)Google Scholar
  7. 7.
    Lipman, Y., Funkhouser, T.: M\(\ddot{\mbox{o}}\)bius voting for surface correspondence. TOG (Proc. SIGGRAPH) 28, 72:1–72:12 (2009)CrossRefGoogle Scholar
  8. 8.
    Zeng, Y., Wang, C., Wang, Y., Gu, X., Samaras, D., Paragios, N.: Dense non-rigid surface registration using high-order graph matching. In: CVPR, pp. 382–389 (2010)Google Scholar
  9. 9.
    Kim, V., Lipman, Y., Funkhouser, T.: Blended intrinsic maps. TOG 30, 79:1–79:12 (2011)Google Scholar
  10. 10.
    Tung, T., Matsuyama, T.: Dynamic surface matching by geodesic mapping for 3d animation transfer. In: CVPR, pp. 1402–1409 (2010)Google Scholar
  11. 11.
    Huang, Q., Adams, B., Wicke, M., Guibas, L.J.: Non-rigid registration under isometric deformations. In: SGP, pp. 1149–1458 (2008)Google Scholar
  12. 12.
    Zhang, H., Sheffer, A., Cohen-Or, D., Zhou, Q., van Kaick, O., Tagliasacchi, A.: Deformation-driven shape correspondence. CGF (Proc. SGP) 27, 1393–1402 (2008)Google Scholar
  13. 13.
    Bronstein, A., Bronstein, M., Bronstein, M., Kimmel, R.: Numerical geometry of non-rigid shapes. Springer (2008)Google Scholar
  14. 14.
    Ovsjanikov, M., Merigot, Q., Memoli, F., Guibas, L.: One point isometric matching with the heat kernel. CGF (Proc. SGP) 29, 1555–1564 (2010)Google Scholar
  15. 15.
    Cazals, F., Pouget, M.: Estimating differential quantities using polynomial fitting of osculating jets. In: SGP, pp. 177–187 (2003)Google Scholar
  16. 16.
    Forman, R.: Morse theory for cell-complexes. Adv. in Math. 134, 90–145 (1998)MathSciNetzbMATHCrossRefGoogle Scholar
  17. 17.
    Robins, V., Wood, P., Sheppard, A.: Theory and algorithms for constructing discrete morse complexes from grayscale digital images. TPAMI 33, 1646–1658 (2011)CrossRefGoogle Scholar
  18. 18.
    Stošić, M., Marques, M., Costeira, J.: Convex solution of a permutation problem. Linear Algebra and its Applications 434, 361–369 (2011)MathSciNetzbMATHCrossRefGoogle Scholar
  19. 19.
    Kuhn, H.: The hungarian method for the assignment problem. Naval Research Logistics Quarterly 2, 83–97 (1955)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Leordeanu, M., Hebert, M.: A spectral technique for correspondence problems using pairwise constraints. In: ICCV, pp. 1482–1489 (2005)Google Scholar
  21. 21.
    Surazhsky, V., Surazhsky, T., Kirsanov, D., Gortler, S., Hoppe, H.: Fast exact and approximate geodesics on meshes. TOG (Proc. SIGGRAPH) 24, 553–560 (2005)CrossRefGoogle Scholar
  22. 22.
    Yin, L., Wei, X., Sun, Y., Wang, J., Rosato, M.: A 3d facial expression database for facial behavior research. In: FG, pp. 211–216 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ying Yang
    • 1
    • 2
  • David Günther
    • 1
    • 3
  • Stefanie Wuhrer
    • 3
    • 1
  • Alan Brunton
    • 3
    • 4
  • Ioannis Ivrissimtzis
    • 2
  • Hans-Peter Seidel
    • 1
  • Tino Weinkauf
    • 1
  1. 1.MPI InformatikSaarbrückenGermany
  2. 2.Durham UniversityDurhamUK
  3. 3.Saarland UniversitySaarbrückenGermany
  4. 4.University of OttawaOttawaCanada

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