Skip to main content
SpringerLink
Log in

Exploring contextual information for view-wised 3D model retrieval

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Recently, with the rapid development of digital technologies and its wide application, 3D model retrieval is becoming more and more important in graphic communities. In this task, how to effectively represent the 3D model and how to robustly measure similarity between pair-wise models are two crucial problems. In previous work, most papers dedicated to researching how to effectively using the visualize features to represent 3D model and using the visual information to measure the similarity. However, visual feature can not represent 3D model well because of the model variations in poses and illumination. To address this task, we propose an novel framework, which utilizes the visual and contextual information to construct the rank graphs and fuses these two graphs to enhance the similarity measure. When fusing visual and contextual information, we define four strategies to measure the similarity among models according to the relation between the query model and the gallery models. The extensive experimental results demonstrate the superiority of our proposed method compare against the state of the arts.

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

Similar content being viewed by others

References

  1. Ankerst M, Kastenmüller G, Kriegel H-P, Seidl T (1999) 3d shape histograms for similarity search and classification in spatial databases. In: Advances in Spatial Databases, 6th International Symposium, pp 207–226

  2. Ansary TF, Daoudi M, Vandeborre J-P (2007) A bayesian 3-d search engine using adaptive views clustering. IEEE Trans Multimed 9(1):78–88

    Article  Google Scholar 

  3. Chen D-Y, Tian X-P, Shen Y-T, Ouhyoung M (2003) On visual similarity based 3d model retrieval. Comput Graph Forum 22:223–232

    Article  Google Scholar 

  4. Feng Y, Zhang Z, Zhao X, Ji R, Gao Y (2018) GVCNN: group-view convolutional neural networks for 3d shape recognition. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, pages 264–272. IEEE Computer Society

  5. Gao Y, Dai Q, Wang M, Zhang N (2011) 3d model retrieval using weighted bipartite graph matching. Sig Proc Image Comm 26(1):39–47

  6. Gao Y, Tang J, Hong R, Yan S, Dai Q, Zhang N, Chua T-S (2012) Camera constraint-free view-based 3-d object retrieval. IEEE Trans Image Process 21 (4):2269–2281

    Article  MathSciNet  Google Scholar 

  7. Gao Y, Dai Q (2014) View-based 3d object retrieval: Challenges and approaches. IEEE MultiMedia 21(3):52–57

    Article  Google Scholar 

  8. Gao Y, Wang M, Ji R, Wu X, Dai Q (2014) 3-d object retrieval with hausdorff distance learning. IEEE Trans Ind Electron 61(4):2088–2098

    Article  Google Scholar 

  9. Giorgi D, Mortara M, Spagnuolo M (2010) 3d shape retrieval based on best view selection. In: Proceedings of the ACM workshop on 3D object retrieval, 3DOR. ACM, pp 9–14

  10. Guo H, Wang J, Gao Y, Li J, Lu H (2016) Multi-view 3d object retrieval with deep embedding network 25:5526–5537

  11. He X, Zhou Y, Zhou Z, Bai S, Bai X (2018) Triplet-center loss for multi-view 3d object retrieval. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pages 1945–1954. IEEE Computer Society

  12. Hilaga M, Shinagawa Y, Komura T, Kunii TL (2001) Topology matching for fully automatic similarity estimation of 3d shapes. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, pp 203–212

  13. Hong C, Yu J, You J, Chen X, Tao D (2015) Multi-view ensemble manifold regularization for 3d object recognition. Inf Sci 320:395–405

    Article  MathSciNet  Google Scholar 

  14. Ip CY, Lapadat D, Sieger L, Regli WC (2002) Using shape distributions to compare solid models. In: Seventh ACM Symposium on Solid Modeling and Applications. ACM, pp 273–280

  15. Kim W-Y, Kim Y-S (2000) A region-based shape descriptor using zernike moments. Proc Sig Image Comm 16(1-2):95–102

    Article  Google Scholar 

  16. Khotanzad A, Hong YH (1990) Invariant image recognition by zernike moments. IEEE Trans Pattern Anal Mach Intell 12(5):489–497

    Article  Google Scholar 

  17. Krizhevsky A, Sutskever I, Hinton GE. (2012) Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp 1106–1114

  18. Leibe B, Schiele B (2003) Analyzing appearance and contour based methods for object categorization. In: 2003 IEEE, Computer Society Conference on Computer Vision and Pattern Recognition, pp 409–415

  19. Liu A, Nie W, Gao Y, Su Y (2016) Multi-modal clique-graph matching for view-based 3d model retrieval. IEEE Trans Image Process 5:2103–2116

    Article  MathSciNet  Google Scholar 

  20. Liu A, Nie W, Gao Y, Su Y (2017) 3-d model retrieval: View-based A benchmark. IEEE Trans Cybern PP(99):1–13

    Google Scholar 

  21. Liu A, Nie W, Gao Y, Su Y (2017) View-based 3-d model retrieval: A benchmark. IEEE Transactions on Cybernetics

  22. Lu K, Wang Q, Xue J, Pan W (2014) 3d model retrieval and classification by semi-supervised learning with content-based similarity. Inf Sci 281:703–713

    Article  MathSciNet  Google Scholar 

  23. Mu̇ller H, Mu̇ller W, Squire D, Marchand-maillet S, Pun T (2001) Performance evaluation in content-based image retrieval: overview and proposals. Pattern Recogn Lett 22(5):593–601

    Article  Google Scholar 

  24. Nie W, Liu A, Gao Zx, Su Y (2015) Clique-graph matching by preserving global & local structure. In: IEEE, Conference on Computer Vision and Pattern Recognition, pp 4503–4510

  25. Nie W, Xiang S, Liu A (2018) Multi-scale cnns for 3d model retrieval. Multimed Tools Appl 77(17):22953–22963

    Article  Google Scholar 

  26. Persoon E, Fu K-S (1977) Shape discrimination using fourier descriptors. IEEE Trans Syst Man Cybern 7(3):170–179

    Article  MathSciNet  Google Scholar 

  27. Qi CR, Yi L, Su H, Guibas LJ (2017) Pointnet+ +: Deep hierarchical feature learning on point sets in a metric space. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems, pp 5099–5108

  28. Su H, Maji S, Kalogerakis E, Learned-Miller EG. (2015) Multi-view convolutional neural networks for 3d shape recognition. In: IEEE, International Conference on Computer Vision, pp 945–953

  29. Wang M, Gao Y, Lu K, Rui Y (2013) View-based discriminative probabilistic modeling for 3d object retrieval and recognition. IEEE Trans Image Process 22(4):1395–1407

    Article  MathSciNet  Google Scholar 

  30. Wang X, Nie W (2015) 3d model retrieval with weighted locality-constrained group sparse coding. Neurocomputing 151:620–625

    Article  Google Scholar 

  31. Wang D, Wang B, Zhao S, Yao H, Liu H (2017) View-based 3d object retrieval with discriminative views. Neurocomputing 252:58–66

    Article  Google Scholar 

  32. Yang L, Albregtsen F (1996) Fast and exact computation of cartesian geometric moments using discrete green’s theorem. Pattern Recogn 29(7):1061–1073

    Article  Google Scholar 

  33. Yi L, Wang X, Wang H-y, Zha H, Qin H (2010) Learning robust similarity measures for 3d partial shape retrieval. Int J Comput Vis 89(2-3):408–431

    Article  Google Scholar 

  34. Yi F, Xie J, Dai G, Wang M, Zhu F, Xu T, Wong EK (2015) 3d deep shape descriptor. In: IEEE, Conference on Computer Vision and Pattern Recognition, pp 2319–2328

  35. Zhang D, Lu G (2002) Generic fourier descriptor for shape-based image retrieval. In: Proceedings of the 2002 IEEE, International Conference on Multimedia and Expo, pp 425–428

  36. Zhao S, Yao H, Zhang Y, Wang Y, Liu S (2015) View-based 3d object retrieval via multi-modal graph learning. Signal Process 112:110–118

    Article  Google Scholar 

  37. Zhu Z, Wang X, Bai S, Yao C, Bai X (2016) Deep learning representation using autoencoder for 3d shape retrieval. Neurocomputing 204:41–50

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (61772359, 61872267, 61902277), the grant of Tianjin New Generation Artificial Intelligence Major Program (19ZXZNGX00110, 18ZXZNGX00150), the Open Project Program of the State Key Lab of CAD & CG, Zhejiang University (Grant No. A2005, A2012).

Author information

Authors and Affiliations

  1. School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China

    Wenhui Li, Yuting Su & Zhenlan Zhao

  2. Tianjin Key Laboratory of Animal and Plant Resistance/College of Life Science, Tianjin Normal University, Tianjin, 300387, China

    Tong Hao

  3. National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data (PSRPC), CAEIT, Beijing, China

    Yangyang Li

Authors
  1. Wenhui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuting Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhenlan Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tong Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yangyang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tong Hao.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, W., Su, Y., Zhao, Z. et al. Exploring contextual information for view-wised 3D model retrieval. Multimed Tools Appl 80, 16397–16412 (2021). https://doi.org/10.1007/s11042-020-08967-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-08967-7

Keywords

Search

Navigation