Graph-Based Deformable 3D Object Matching

  • Bertram Drost
  • Slobodan Ilic
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9358)


We present a method for efficient detection of deformed 3D objects in 3D point clouds that can handle large amounts of clutter, noise, and occlusion. The method generalizes well to different object classes and does not require an explicit deformation model. Instead, deformations are learned based on a few registered deformed object instances. The approach builds upon graph matching to find correspondences between scene and model points. The robustness is increased through a parametrization where each graph vertex represents a full rigid transformation. We speed up the matching through greedy multi-step graph pruning and a constant-time feature matching. Quantitative and qualitative experiments demonstrate that our method is robust, efficient, able to detect rigid and non-rigid objects and exceeds state of the art.


  1. 1.
    Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (FPFH) for 3D registration. In: ICRA (2009)Google Scholar
  2. 2.
    Wahl, E., Hillenbrand, G., Hirzinger, G.: Surflet-pair-relation histograms: a statistical 3d-shape representation for rapid classification. In: 3DIM (2003)Google Scholar
  3. 3.
    Drost, B., Ulrich, M., Navab, N., Ilic, S.: Model globally, match locally: efficient and robust 3D object recognition. In: CVPR (2010)Google Scholar
  4. 4.
    Leordeanu, M., Hebert, M.: A spectral technique for correspondence problems using pairwise constraints. In: ICCV (2005)Google Scholar
  5. 5.
    Myronenko, A., Song, X.: Point set registration: coherent point drift. PAMI 32(12), 2262–2275 (2010)CrossRefGoogle Scholar
  6. 6.
    Chui, H., Rangarajan, A.: A new point matching algorithm for non-rigid registration. CVIU 89(2), 114–141 (2003)zbMATHGoogle Scholar
  7. 7.
    Anguelov, D., Srinivasan, P., Pang, H.C., Koller, D., Thrun, S., Davis, J.: The correlated correspondence algorithm for unsupervised registration of nonrigid surfaces. NIPS. 17, 33–40 (2004)Google Scholar
  8. 8.
    Ruiz-Correa, S., Shapiro, L.G., Meila, M.: A new paradigm for recognizing 3-d object shapes from range data. In: ICCV, pp. 1126–1133. Citeseer (2003)Google Scholar
  9. 9.
    Conte, D., Foggia, P., Sansone, C., Vento, M.: Thirty years of graph matching in pattern recognition. IJPRAI 18(03), 265–298 (2004)Google Scholar
  10. 10.
    Duchenne, O., Bach, F., Kweon, I.S., Ponce, J.: A tensor-based algorithm for high-order graph matching. PAMI 33(12), 2383–2395 (2011)CrossRefGoogle Scholar
  11. 11.
    Berg, A.C., Berg, T.L., Malik, J.: Shape matching and object recognition using low distortion correspondences. In: CVPR (2005)Google Scholar
  12. 12.
    Zass, R., Shashua, A.: Probabilistic graph and hypergraph matching. In: CVPR (2008)Google Scholar
  13. 13.
    Chertok, M., Keller, Y.: Efficient high order matching. PAMI 32(12), 2205–2215 (2010)CrossRefGoogle Scholar
  14. 14.
    Leordeanu, M., Zanfir, A., Sminchisescu, C.: Semi-supervised learning and optimization for hypergraph matching. In: ICCV, pp. 2274–2281. IEEE (2011)Google Scholar
  15. 15.
    Lee, J., Cho, M., Lee, K.M.: Hyper-graph matching via reweighted random walks. In: CVPR, pp. 1633–1640. IEEE (2011)Google Scholar
  16. 16.
    Passalis, G., Kakadiaris, I.A., Theoharis, T.: Intraclass retrieval of nonrigid 3D objects: application to face recognition. PAMI 29(2), 218–229 (2007)CrossRefGoogle Scholar
  17. 17.
    Mahmoudi, M., Sapiro, G.: Three-dimensional point cloud recognition via distributions of geometric distances. Graph. Models 71(1), 22–31 (2009)CrossRefGoogle Scholar
  18. 18.
    Hinterstoisser, S., Cagniart, C., Ilic, S., Sturm, P., Navab, N., Fua, P., Lepetit, V.: Gradient response maps for real-time detection of textureless objects. PAMI 34(5), 876–888 (2012)CrossRefGoogle Scholar
  19. 19.
    Turk, G., Levoy, M.: Zippered polygon meshes from range images. In: Proceedings 21st Annual Conference on Computer Graphics and Interactive Techniques, p. 318. ACM (1994)Google Scholar
  20. 20.
    Sederberg, T.W., Parry, S.R.: Free-form deformation of solid geometric models. In: ACM Siggraph Computer Graphics, vol. 20, pp. 151–160. ACM (1986)Google Scholar
  21. 21.
    Mian, A.S., Bennamoun, M., Owens, R.A.: Automatic correspondence for 3D modeling: an extensive review. Int. J. Shape Model. 11(2), 253 (2005)CrossRefzbMATHGoogle Scholar
  22. 22.
    Johnson, A.E., Hebert, M.: Using spin images for efficient object recognition in cluttered 3d scenes. PAMI 21(5), 433–449 (1999)CrossRefGoogle Scholar
  23. 23.
    Mian, A.S., Bennamoun, M., Owens, R.: Three-dimensional model-based object recognition and segmentation in cluttered scenes. PAMI 28(10), 1584–1601 (2006)CrossRefGoogle Scholar
  24. 24.
    Mian, A., Bennamoun, M., Owens, R.: On the repeatability and quality of keypoints for local feature-based 3D object retrieval from cluttered scenes. Int. J. Comput. Vision 89(2–3), 348–361 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  1. 1.MVTec Software GmbHMunichGermany
  2. 2.Siemens AGMunichGermany

Personalised recommendations