Skip to main content
SpringerLink
Log in

Nonrigid 3D shape retrieval using deep auto-encoders

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

The soaring popularity of deep learning in a wide variety of fields ranging from computer vision and speech recognition to self-driving vehicles has sparked a flurry of research interest from both academia and industry. In this paper, we propose a deep learning approach to 3D shape retrieval using a multi-level feature learning paradigm. Low-level features are first extracted from a 3D shape using spectral graph wavelets. Then, mid-level features are generated via the bag-of-features model by employing locality-constrained linear coding as a feature coding method, in conjunction with the biharmonic distance and intrinsic spatial pyramid matching in a bid to effectively measure the spatial relationship between each pair of the bag-of-feature descriptors. Finally, high-level shape features are learned by applying a deep auto-encoder on mid-level features. Extensive experiments on SHREC-2014 and SHREC-2015 datasets demonstrate the much better performance of the proposed framework in comparison with state-of-the-art methods.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Rustamov R (2007) Laplace-Beltrami eigenfunctions for deformation invariant shape representation. In: Proc. Symp. Geometry Processing, pp 225–233

  2. Sun J, Ovsjanikov M, Guibas L (2009) A concise and provably informative multi-scale signature based on heat diffusion. Comput Graphics Forum 28(5):1383–1392

    Article  Google Scholar 

  3. Aubry M, Schlickewei U, Cremers D (2011) The wave kernel signature: A quantum mechanical approach to shape analysis. In: Proc. Computational Methods for the Innovative Design of Electrical Devices, pp 1626–1633

  4. Bronstein M, Kokkinos I (2010) Scale-invariant heat kernel signatures for non-rigid shape recognition. In: Proceedings of the CVPR, pp 1704–1711

  5. Li C, Ben Hamza A (2013) A multiresolution descriptor for deformable 3D shape retrieval. Vis Comput 29:513–524

    Article  Google Scholar 

  6. Chaudhari A, Leahy R, Wise B, Lane N, Badawi R, Joshi A (2014) Global point signature for shape analysis of carpal bones. Phys Med Biol 59:961–973

    Article  Google Scholar 

  7. Ye J, Yu Y (2015) A fast modal space transform for robust nonrigid shape retrieval. Vis Comput 32 (5):553–568

    Article  Google Scholar 

  8. Mohamed W, Ben Hamza A (2016) Deformable 3D shape retrieval using a spectral geometric descriptor. Appl Intell 45(2):2213–229

    Article  Google Scholar 

  9. Pickup D, Sun X, Rosin P, Martin R, Cheng Z, Lian Z, Aono M, Ben Hamza A, Bronstein A, Bronstein M, Bu S, Castellani U, Cheng S, Garro V, Giachetti V, Godil A, Han J, Johan H, Lai L, Li L, Li C, Li H, Litman R, Liu X, Liu Z, Lu Y, Tatsuma A, Ye J (2014) SHREC, 14 track: Shape retrieval of non-rigid 3D human models. In: Proc. Eurographics Workshop on 3D Object Retrieval, pp 1–10

  10. Lian JZZ, Choi S, ElNaghy H, El-Sana J, Furuya T, Giachetti A, Isaia RGL, Lai L, Li C, Li H, Limberger F, Martin R, Nakanishi R, Nonato ANL, Ohbuchi R, Pevzner K, Pickup D, Rosin P, Sharf A, Sun L, Sun X, Tari S, Unal G, Wilson R (2015) SHREC’15 track: Non-rigid 3D shape retrieval. In: Proc. Eurographics Workshop on 3D, Object Retrieval, pp 1–14

  11. Reuter M, Wolter F, Peinecke N (2006) Laplace-Beltrami spectra as Shape-DNA’ of surfaces and solids. Comput Aided Des 38(4):342–366

    Article  Google Scholar 

  12. Bronstein A, Bronstein M, Guibas L, Ovsjanikov M (2011) Shape Google: Geometric words and expressions for invariant shape retrieval. ACM Trans Graphics 1:30

    Google Scholar 

  13. Litman R, Bronstein A, Bronstein M, Castellani U (2014) Supervised learning of bag-of-features shape descriptors using sparse coding. Comput Graphics Forum 33(5):127–136

    Article  Google Scholar 

  14. Noda K, Yamaguchi Y, Nakadai K, Okuno H, Ogata T (2015) Audio-visual speech recognition using deep learning. Appl Intell 42(4):722–737

    Article  Google Scholar 

  15. Wu Z, Song S, Khosla A, Yu F, Zhang L, Tang X, Xiao J (2015) 3D ShapeNets: A deep representation for volumetric shapes. In: Proc. CVPR, pp 1912–1920

  16. Savva M, Yu F, Su H, aono M, Chen B, Cohen-Or D, Deng W, Su H, Bai S, Bai X, Fish JHN, Kalogerakis E, Learned-Miller E, Li Y, Liao M, Maji S, Wang Y, Zhang N, Zhou Z (2016) SHREC’16 track: Large-scale 3D shape retrieval from ShapeNet Core55. In: Proc. Eurographics Workshop on 3D Object Retrieval

  17. Qi C, Su H, Nießner M., Dai A, Yan M, Guibas L (2016) Volumetric and multi-view CNNs for object classification on 3D data. In: Proc. CVPR

  18. Zhu Z, Wang X, Bai S, Yao C, Bai X (2016) Deep learning representation using autoencoder for 3D shape retrieval Neurocomputing

  19. Fang Y, Xie J, Dai G, Wang M, Zhu F, Xu T, Wong E (2015) 3D deep shape descriptor. In: Proc. CVPR, pp 2319–2328

  20. Bu S, Liu Z, Han J, Wu J, Ji R (2014) Learning high-level feature by deep belief networks for 3-D model retrieval and recognition. IEEE Trans Multimedia 24(16):2154–2167

    Article  Google Scholar 

  21. Lipman Y, Rustamov R, Funkhouser T (2010) Biharmonic distance. ACM Trans Graphics 29(3):1–11

    Article  Google Scholar 

  22. Li C, Ben Hamza A (2013) Intrinsic spatial pyramid matching for deformable 3D shape retrieval. Int Journal Multimedia Information Retrieval 2:261–271

    Article  Google Scholar 

  23. Dong W, Li X, Zhang D, Shi G (2010) Sparsity-basedx image denoising via dictionary learning and structural clustering. In: Proc. CVPR, pp 3360–3367

  24. Ben Hamza A, Krim H (2006) Geodesic matching of triangulated surfaces. IEEE Trans Image Processing 15(8):2249–2258

    Article  Google Scholar 

  25. Lian Z, Godil A, Bustos B, Daoudi M, Hermans J, Kawamura S, Kurita Y, Lavoué G., Nguyen H, Ohbuchi R, Ohkita Y, Ohishi Y, Porikli F, Reuter M, Sipiran I, Smeets D, Suetens P, Tabia H, Vandermeulen D (2011) SHREC’11 track: Shape retrieval on non-rigid 3D watertight meshes. In: Proc. Eurographics/ACM SIGGRAPH Symp 3D. Object Retrieval, pp 79–88

  26. Giachetti A, Lovato C (2012) Radial symmetry detection and shape characterization with the multiscale area projection transform. Comput Graphics Forum 31(5):1669–1678

    Article  Google Scholar 

  27. Pickup D, Sun X, Rosin P, Martin R (2015) Geometry and context for semantic correspondences and functionality recognition in manmade 3D shapes. Pattern Recogn 48(8):2500–2512

    Article  Google Scholar 

  28. van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9:2579–2605

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada

    Hamed Ghodrati & A. Ben Hamza

Authors
  1. Hamed Ghodrati
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Ben Hamza
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Ben Hamza.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ghodrati, H., Hamza, A.B. Nonrigid 3D shape retrieval using deep auto-encoders. Appl Intell 47, 44–61 (2017). https://doi.org/10.1007/s10489-016-0880-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-016-0880-1

Keywords

Search

Navigation