Template-Guided 3D Fragment Reassembly Using GDS

  • Congli Yin
  • Mingquan ZhouEmail author
  • Yachun Fan
  • Wuyang Shui
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 875)


Computer-aided fragment reassembly becomes more and more significant in recent years. The state of the art methods mainly utilize the fracture surface of the fragment. However, some fracture surfaces are often eroded and the features are not discriminative enough for matching. In this paper, we proposed a template-guided 3D fragment reassembly algorithm using Geodesic Disk Spectrum (GDS), which conducts matching between the intact surface of the fragment and the template. A two-step procedure is proposed for the first time with GDS-based matching and ICP-based registration for the reassembly task. The largest enclosed geodesic disk of the fragment is extracted and the matching to the template is found by GDS. In order to reduce the computational complexity, a k-layer Normal Distribution Descriptor (NDD) is also proposed. Transformation of the matched geodesic disks is obtained using the Iterative Closest Points (ICP) algorithm, and the registration between the fragment and the template is achieved. Our algorithm has been tested on various fragments and accurate results are obtained. A higher precision is achieved by comparing with existing algorithms, which proves the efficiency.


Fragment reassembly Template-guided Geodesic disk spectrum 


  1. 1.
    Huang, Q.-X., Flory, S., Gelfand, N.: Reassembling fractured objects by geometric matching. In: Proceedings of ACM SIGGRAPH 2006 Papers, pp. 569–578. ACM, New York (2006)Google Scholar
  2. 2.
    Zhang, K., Yu, W., Manhein, M.: Reassembling 3D thin shells using integrated template guidance and fracture region matching. In: Proceedings of ACM SIGGRAPH 2015 Posters, pp. 88:1–88:1 (2015)Google Scholar
  3. 3.
    Zhang, K., Yu, W., Manhein, M.: 3D fragment reassembly using integrated template guidance and fracture-region matching. In: IEEE International Conference on Computer Vision, pp. 2138–2146. IEEE Computer Society (2015)Google Scholar
  4. 4.
    Li, X., Yin, Z., Wei, L.: Symmetry and template guided completion of damaged skulls. Comput. Graph. 35(4), 885–893 (2011)CrossRefGoogle Scholar
  5. 5.
    Cooper, D.B., Willis, A., Andrews, S.: Assembling virtual pots from 3D measurements of their fragments. In: Virtual Reality, Archeology, and Cultural Heritage, pp. 241–254 (2001)Google Scholar
  6. 6.
    Willis, A.R., Cooper, D.B.: Bayesian assembly of 3D axially symmetric shapes from fragments. In: CVPR (2004)Google Scholar
  7. 7.
    Yin, Z., Wei, L., Manhein, M., Li, X.: An automatic assembly and completion framework for fragmented skulls. In: International Conference on Computer Vision, pp. 2532–2539 (2011)Google Scholar
  8. 8.
    Besl, P.J., Mckay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(3), 239–256 (1992)CrossRefGoogle Scholar
  9. 9.
    Liu, Z., Shuhui, B., Zhou, K.: A survey on partial retrieval of 3D shapes. J. Comput. Sci. Technol. 28, 836–851 (2013)CrossRefGoogle Scholar
  10. 10.
    Johnson, A.E., Hebert, M.: Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Trans. Pattern Anal. Mach. Intell. 21(5), 433–449 (2002)CrossRefGoogle Scholar
  11. 11.
    Malassiotis, S., Strintzis, M.G.: Snapshots: a novel local surface descriptor and matching algorithm for robust 3D surface alignment. IEEE Trans. Pattern Anal. Mach. Intell. 29(7), 1285–1290 (2007)CrossRefGoogle Scholar
  12. 12.
    Kazhdan, M., Funkhouser, T., Rusinkiewicz, S.: Rotation invariant spherical harmonic representation of 3D shape descriptors. 43(2), 156–164 (2003)Google Scholar
  13. 13.
    Hu, J., Hua, J.: Salient spectral geometric features for shape matching and retrieval. Vis. Comput. 25(5–7), 667–675 (2009)CrossRefGoogle Scholar
  14. 14.
    Wu, H.Y., Zha, H., Luo, T.: Global and local isometry-invariant descriptor for 3D shape comparison and partial matching. In: Computer Vision and Pattern Recognition, pp. 438–445. IEEE (2010)Google Scholar
  15. 15.
    Dubrovina, A., Kimmel, R.: Matching shapes by eigendecomposition of the Laplace-Beltrami operator. In: Proceedings of the Fifth International Symposium on 3D Data Processing Visualization and Transmission (2010)Google Scholar
  16. 16.
    Lavoue, G.: Bag of words and local spectral descriptor for 3D partial shape retrieval. In: Eurographics Conference on 3D Object Retrieval Eurographics Association, pp. 41–48 (2011)Google Scholar
  17. 17.
    Sun, J., Ovsjanikov, M., Guibas, L.: A concise and provably informative multi scale signature based on heat diffusion. In: Proceedings of Computer Graphics Forum, pp. 1383–1392 (2009)Google Scholar
  18. 18.
    Du, G., Yin, C., Zhou, M.: Part-in-whole matching of rigid 3D shapes using geodesic disk spectrum. Multimedia Tools Appl. 3, 1–21 (2017)Google Scholar
  19. 19.
    Yu, W., Li, M., Li, X.: Fragmented skull modeling using heat kernels. Graph. Models 74(4), 140–151 (2012)CrossRefGoogle Scholar
  20. 20.
    Koenderink, J.J., Van Doorn, A.J.: Surface shape and curvature scales. Image Vis. Comput. 10(8), 557–564 (1992)CrossRefGoogle Scholar
  21. 21.
    Dorai, C., Jain, A.K.: COSMOS—a representation scheme for 3D free-form objects. IEEE Trans. Pattern Anal. Mach. Intell. 19(10), 1115–1130 (1997)CrossRefGoogle Scholar
  22. 22.
    Mitchell, J.S.B., Mount, D.M., Papadimitriou, C.H.: The discrete geodesic problem. SIAM J. Comput. 16(4), 647–668 (1987)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Martinek, M., Grosso, R., Greiner, G.: Interactive partial 3D shape matching with geometric distance optimization. Vis. Comput. 31(2), 223–233 (2015)CrossRefGoogle Scholar
  24. 24.
    Zhong, Y.: Intrinsic shape signatures: a shape descriptor for 3D object recognition. In: Proceedings of IEEE International Conference on Computer Vision Workshops, pp. 689–696 (2009)Google Scholar
  25. 25.
    Tombari, F., Salti, S., Di Stefano, L.: Unique signatures of histograms for local surface description. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6313, pp. 356–369. Springer, Heidelberg (2010). Scholar
  26. 26.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Andreadis, A., Mavridis, P., Papaioannou, G.: Facet extraction and classification for the reassembly of fractured 3D objects (2014)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Congli Yin
    • 1
    • 2
  • Mingquan Zhou
    • 1
    • 2
    Email author
  • Yachun Fan
    • 1
    • 2
  • Wuyang Shui
    • 1
    • 2
  1. 1.College of Information Science and TechnologyBeijing Normal UniversityBeijingChina
  2. 2.Key Laboratory of Digital Protection and Virtual Reality for Cultural HeritageBeijingChina

Personalised recommendations