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A CAD model retrieval framework based on correlation network and relevance ranking

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Abstract

The computer-aided design (CAD) models contain abundant domain knowledge, either structure, material, or process information. An efficient retrieval ability for these reusable design resources will provide designers invaluable support for efficient product development. With this goal, this paper proposed a novel CAD model retrieval framework based on correlation network and relevance ranking. First, a multi-layer network was constructed to express the high-level local correlation between CAD models. Then, the global shape comparison method is employed to determine the CAD models most similar to the query, called the relevant subset. Finally, the relevance ranking based on the Bayesian theory can be performed by analyzing the correlation between the relevant subset and other CAD models. The relevance probability determines which CAD model is the most relevant to the query, and the ranking list can be finally obtained. Experimental results and comparisons with state-of-the-art methods demonstrate the superiority and user-friendliness of the proposed method.

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References

  1. S. Dai, G. Zhao, Y. Yu, P. Zheng, Q. Bao and W. Wang, Ontology-based information modeling method for digital twin creation of as-fabricated machining parts, Robotics and Computer-Integrated Manufacturing, 72 (2021) 220–229.

    Article  Google Scholar 

  2. P. Wang, Y. Li, J. Zhang and J. Yu, An assembly retrieval approach based on shape distributions and earth mover’s distance, International Journal of Advanced Manufacturing Technology, 86 (9–12) (2016) 1–17.

    Google Scholar 

  3. B. Li et al., SHREC’16 track: 3D sketch-based 3D shape retrieval, Eurographics Workshop on 3D Object Retrieval (3DOR), Lisbon (2016).

  4. X. Tan, Y. Fan and R. Guo, Local features and manifold ranking coupled method for sketch-based CAD model retrieval, Frontiers of Computer Science, 12 (5) (2018) 1000–1012.

    Article  Google Scholar 

  5. K. Garrouch and M. N. Omri, Bayesian network-based information retrieval model, 2017 International Conference on High Performance Computing and Simulation (HPCS), Barcelona (2017) 193–200.

  6. R. Kwitt, P. Meerwald and A. Uhl, Efficient texture image retrieval using copulas in a bayesian framework, IEEE Transactions on Image Processing, 20 (7) (2011) 2063–2077.

    Article  MathSciNet  MATH  Google Scholar 

  7. J. Kalpana and R. Krishnamoorthy, Generalized adaptive bayesian relevance feedback for image retrieval in the orthogonal polynomials transform domain, Signal Processing, 92 (12) (2012) 3062–3067.

    Article  Google Scholar 

  8. X. Sun and B. Bischl, Tutorial and survey on probabilistic graphical model and variational inference in deep reinforcement learning, 2019 IEEE Symposium Series on Computational Intelligence, Xiamen (2019) 110–119.

  9. R. Osada, T. Funkhouser, B. Chazelle and D. Dobkin, Matching CAD models with shape distributions, SMI 2001 International Conference on Shape Modeling and Applications, Genova (2001).

  10. H. P. Kriegel, P. Kroger, Z. Mashael, M. Pfeifle, M. Potke and T. Seidl, Effective similarity search on voxelized CAD objects, Eighth International Conference on Database Systems for Advanced Applications, Kyoto (2003) 27–36.

  11. C. Wang, M. Cheng, F. Sohel, M. Bennamoun and J. Li, NormalNet: a voxel-based CNN for 3D object classification and retrieval, Neurocomputing, 323 (2019) 139–147.

    Article  Google Scholar 

  12. K. P. Zhu, Y. S. Wong, W. F. Lu and H. T. Loh, CAD model matching from 2D local invariant features, Computers in Industry, 61 (5) (2010) 432–439.

    Article  Google Scholar 

  13. Y. Lei, Z. Zhou, P. Zhang, Y. Guo, Z. Ma and L. Liu, Deep point-to-subspace metric learning for sketch-based 3D shape retrieva, Pattern Recognition, 96 (2019) 106981.

    Article  Google Scholar 

  14. E. Boyer, A. M. Bronstein, M. M. Bronstein, B. Bustos, T. Darom and R. Horaud, SHREC 2011: robust feature detection and description benchmark, Eurographics Workshop on 3D Object Retrieval, Llandudno (2011).

  15. T. Federico, S. Samuele and D. S. Luigi, Unique shape context for CAD data description, Proc. of the ACM Workshop on CAD Object Retrieval, Bologna (2010) 57–62.

  16. M. A. Savelonas, I. Pratikakis and K. Sfikas, Fisher encoding of differential fast point feature histograms for partial CAD object retrieval, Pattern Recognition, 55 (2016) 114–124.

    Article  Google Scholar 

  17. H. H. Mohamed and S. Belaid, Algorithm BOSS (bag-of-salient local spectrums) for non-rigid and partial CAD object retrieval, Neurocomputing, 168 (2015) 790–798.

    Article  Google Scholar 

  18. A. M. Bronstein, M. M. Bronstein, L. J. Guibas and M. Ovsjanikov, Shape google: geometric words and expressions for invariant shape retrieval, ACM Transactions on Graphics, 30 (1) (2011) 1–20.

    Article  Google Scholar 

  19. C. Xu, S. Zhang, B. Huang, X. Li and R. Huang, NC process reuse oriented effective subpart retrieval approach of 3D CAD models, Computers in Industry, 90 (2017) 1–16.

    Article  Google Scholar 

  20. Z. Zhang, P. Jaiswal and R. Rai, FeatureNet: machining feature recognition based on 3D convolution neural network, Computer-Aided Design, 101 (2018) 12–22.

    Article  Google Scholar 

  21. P. Shi, Q. Qi, Y. Qin, P. J. Scott and X. Jiang, A novel learning-based feature recognition method using multiple sectional view representation, Journal of Intelligent Manufacturing, 31 (5) (2020) 1291–1309.

    Article  Google Scholar 

  22. S. Tao, S. Wang and A. Chen, 3D CAD solid model retrieval based on region segmentation, Multimedia Tools and Applications, 76 (1) (2015) 103–121.

    Article  Google Scholar 

  23. T. Furuya and R. Ohbuchi, Ranking on cross-domain manifold for sketch-based CAD model retrieval, 2013 International Conference on Cyberworlds, Yokohama (2013) 274–281.

  24. T. Komamizu, Random walk-based entity representation learning and re-ranking for entity search, Knowledge and Information Systems, 62 (3) (2020) 2989–3013.

    Article  Google Scholar 

  25. C. Gong, K. Fu, A. Loza, Q. Wu and J. Liu, PageRank tracker: from ranking to tracking, IEEE Transactions on Cybernetics, 44 (6) (2017) 882–893.

    Article  Google Scholar 

  26. W. Zhao, Z. Guan and Z. Liu, Ranking on heterogeneous manifolds for tag recommendation in social tagging services, Neurocomputing, 148 (2015) 521–534.

    Article  Google Scholar 

  27. T. Kubota, Affinity learning with diffusion on tensor product graph, Computing Reviews, 35 (1) (2013) 28–38.

    Google Scholar 

  28. J. Wang, Y. Li, B. Xiang, W. Chao and T. Ning, Learning context-sensitive similarity by shortest path propagation, Pattern Recognition, 44 (10–11) (2011) 2367–2374.

    Article  Google Scholar 

  29. F. Chen, B. Li and L. Li, CAD object retrieval with graph-based collaborative feature learning, Journal of Visual Communication and Image Representation, 58 (2019) 261–268.

    Article  MathSciNet  Google Scholar 

  30. Y. Gao and Q. Dai, Efficient view-based 3-D object retrieval via hypergraph learning, Tsinghua Science and Technology, 19 (3) (2014) 250–256.

    Article  Google Scholar 

  31. Y. Zhang, T. Yamamoto and Y. Dobashi, Multi-scale object retrieval via learning on graph from multimodal data, Neurocomputing, 207 (2016) 684–692.

    Article  Google Scholar 

  32. J. Yang, J. Zhao and Q. Sun, CAD model retrieval using constructive-learning for cross-model correlation, Neurocomputing, 275 (2017) 1–9.

    Article  Google Scholar 

  33. P. Tao, J. Cao, S. Li, X. Liu and L. Liu, Mesh saliency via ranking unsalient patches in a descriptor space, Computers and Graphics, 46 (2015) 264–274.

    Article  Google Scholar 

  34. G. Zhu, Q. Wang, Y. Yuan and P. Yan, SIFT on manifold: an intrinsic description, Neurocomputing, 113 (2013) 227–233.

    Article  Google Scholar 

  35. T. Furuya and R. Ohbuchi, Diffusion-on-manifold aggregation of local features for shape-based CAD model retrieval, Proc of the 5th ACM on International Conference on Multimedia Retrieval, Yamanashi (2015) 171–178.

  36. E. Maslov, M. Batsyn and P. M. Pardalos, Speeding up branch and bound algorithms for solving the maximum clique problem, Journal of Global Optimization, 59 (1) (2014) 1–21.

    Article  MathSciNet  MATH  Google Scholar 

  37. D. Zhou, J. Weston, A. Gretton, O. Bousquet and B. Schölkopf, Ranking on data manifolds, Advances in Neural Information Processing Systems, 16 (2003) 169–176.

    Google Scholar 

  38. A. A. Liu, W. H. Li, W. Z. Nie, Y. An and L. Xu, 3D models retrieval algorithm based on multimodal data, Neurocomputing, 259 (2017) 176–182.

    Article  Google Scholar 

  39. H. Paulheim, Knowledge graph refinement: a survey of approaches and evaluation methods, Semantic Web, 8 (3) (2017) 489–508.

    Article  Google Scholar 

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 52175252), the National Natural Science Foundation of China (No. 52105559), and the Natural Science Foundation of Shaanxi Province (No. 2021JQ-680).

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Authors and Affiliations

  1. School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710072, China

    Baoning Ji, Jie Zhang, Yuan Li & Wenbin Tang

  2. School of Mechanical and Electrical Engineering, Xi’an Polytechnic University, Xi’an, 710600, China

    Wenbin Tang

Authors
  1. Baoning Ji
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  2. Jie Zhang
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  3. Yuan Li
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  4. Wenbin Tang
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Corresponding author

Correspondence to Jie Zhang.

Additional information

Jie Zhang is a Professor in the School of Mechanical Engineering at Northwestern Polytechnical University, P.R. China. He obtained his B.S. in Automatic Control (2002), M.S. (2006), and Ph.D. (2009) in Aeronautics and Astronautics Manufacturing Engineering from Northwestern Polytechnical University, P.R. China. His research interest includes advanced assembly technology, CAD/CAM, aircraft project management, etc.

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Ji, B., Zhang, J., Li, Y. et al. A CAD model retrieval framework based on correlation network and relevance ranking. J Mech Sci Technol 37, 1973–1984 (2023). https://doi.org/10.1007/s12206-023-0334-8

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  • DOI: https://doi.org/10.1007/s12206-023-0334-8

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