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Three-dimensional model retrieval in single category geometry using local ontology created by object part segmentation through deep neural network

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Abstract

3D model retrieval is useful for reusing designs in manufacturing industry. Traditionally, 3D model retrieval has been implemented only in low-level information such as geometry, color, and texture. However high-level semantic information should be used for more accurate retrieval. In this study, a 3D geometry is divided into several parts using PointNet and then the local ontology is constructed by summarizing the characteristics of each part. Then part align similarity, lemma similarity, name similarity, part location similarity, and part size similarity are calculated. Using the values of these similarities, 3D models are retrieved from input query model. This comprehensive retrieval that includes all the similarities is more balanced and shows better performance in nameless models than considering only partial similarities. Through the method in this paper, high-level information and low-level information can be used simultaneously for 3D model retrieval.

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Abbreviations

S T :

Total similarity

S P :

Part align similarity

S L :

Lemma similarity

S N :

Name similarity

S PI :

Part location similarity

S Ps :

Part size similarity

W P :

Weight of part align similarity

W L :

Weight of lemma similarity

W N :

Weight of name similarity

W PI :

Weight of part location similarity

W Ps :

Weight of part size similarity

References

  1. F. Qin, S. Gao, X. Yang, M. Li and J. Bai, An ontology-based semantic retrieval approach for heterogeneous 3D CAD models, Advanced Engineering Informatics, 30(4) (2016) 751–768.

    Article  Google Scholar 

  2. D. V. Vranic and D. Saupe, 3D model retrieval, Ph.D. Thesis, University of Leipzig, Germany (2004).

    MATH  Google Scholar 

  3. S. Jayanti, Y. Kalyanaraman, N. Iyer and K. Ramani, Developing an engineering shape benchmark for CAD models, Computer-Aided Design, 38(9) (2006) 939–953.

    Article  Google Scholar 

  4. N. Iyer, S. Jayanti, K. Lou, Y. Kalyanaraman and K. Ramani, Three-dimensional shape searching: state-of-the-art review and future trends, Computer-Aided Design, 37(5) (2005) 509–530.

    Article  Google Scholar 

  5. N. F. Noy, Semantic integration: a survey of ontology-based approaches, ACM Sigmod Record, 33(4) (2004) 65–70.

    Article  Google Scholar 

  6. M. Huang, S. Sui, W. Mou, S. Zhang and W. Cao, Three-dimensional cad model retrieval algorithm based on ontology, Procedia CIRP, 56 (2016) 590–593.

    Article  Google Scholar 

  7. D.-Y. Chen, X.-P. Tian, Y.-T. Shen and M. Ouhyoung, On visual similarity based 3D model retrieval, Computer Graphics Forum, Wiley Online Library, 22 (2003) 223–232.

    Article  Google Scholar 

  8. A.-A. Liu, H.-Y. Zhou, M.-J. Li and W.-Z. Nie, 3D model retrieval based on multi-view attentional convolutional neural network, Multimedia Tools and Applications (2019) 1–13.

  9. B. Shi, S. Bai, Z. Zhou and X. Bai, Deeppano: deep panoramic representation for 3-D shape recognition, IEEE Signal Processing Letters, 22(12) (2015) 2339–2343.

    Article  Google Scholar 

  10. H. Su, S. Maji, E. Kalogerakis and E. Learned-Miller, Multiview convolutional neural networks for 3D shape recognition, Proceedings of the IEEE International Conference on Computer Vision (2015) 945–953.

  11. E. Johns, S. Leutenegger and A. J. Davison, Pairwise decomposition of image sequences for active multi-view recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016) 3813–3822.

  12. C. R. Qi, H. Su, K. Mo and L. J. Guibas, Pointnet: deep learning on point sets for 3D classification and segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017) 652–660.

  13. Y. Zhou and O. Tuzel, Voxelnet: end-to-end learning for point cloud based 3D object detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018) 4490–4499.

  14. S. Auer, C. Bizer, G. Kobilarov, J. Lehmann, R. Cyganiak and Z. Ives, DBpedia: a nucleus for a web of open data, The Semantic Web, Springer (2007) 722–735.

  15. G. A. Miller, Wordnet: a lexical database for English, Communications of the ACM, 38(11) (1995) 39–41.

    Article  Google Scholar 

  16. L. Yi, V. G. Kim, D. Ceylan, I. Shen, M. Yan, H. Su, C. Lu, Q. Huang, A. Sheffer and L. Guibas, A scalable active framework for region annotation in 3D shape collections, ACM Transactions on Graphics (TOG), 35(6) (2016) 210.

    Article  Google Scholar 

  17. A. X. Chang et al., Shapenet: an information-rich 3D model repository, arXiv preprint arXiv:1512.03012 (2015).

  18. J.-B. Lamy, Owlready: ontology-oriented programming in python with automatic classification and high level constructs for biomedical ontologies, Artificial Intelligence in Medicine, 80 (2017) 11–28.

    Article  Google Scholar 

  19. S. Benabderrahmane, M. Smail-Tabbone, O. Poch, A. Napoli and M.-D. Devignes, Intelligo: a new vector-based semantic similarity measure including annotation origin, BMC Bioinformatics, 11(1) (2010) 588.

    Article  Google Scholar 

  20. G. Zhu and C. A. Iglesias, Computing semantic similarity of concepts in knowledge graphs, IEEE Transactions on Knowledge and Data Engineering, 29(1) (2017) 72–85.

    Article  Google Scholar 

  21. R. Mihalcea, C. Corley and C. Strapparava, Corpus-based and knowledge-based measures of text semantic similarity, AAAI, 6 (2006) 775–780.

    Google Scholar 

  22. J. Ye, Cosine similarity measures for intuitionistic fuzzy sets and their applications, Mathematical and Computer Modelling, 53(1–2) (2011) 91–97.

    Article  MathSciNet  Google Scholar 

  23. C. Luo, J. Zhan, X. Xue, L. Wang, R. Ren and Q. Yang, Cosine normalization: using cosine similarity instead of dot product in neural networks, International Conference on Artificial Neural Networks, Springer (2018) 382–391.

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Acknowledgments

This work is supported by Knowledge Based Design Laboratory, Yonsei University, Seoul, Republic of Korea.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Yonsei University, Seoul, 03722, Korea

    Hojoon Son & Soo-Hong Lee

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  1. Hojoon Son
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  2. Soo-Hong Lee
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Correspondence to Soo-Hong Lee.

Additional information

Hojoon Son received his M.S. degree in Mechanical Engineering from Yonsei University at Seoul, South Korea in 2020. He also received a B.S. degree, double major in Mechanical Engineering and Electrical & Electronic Engineering from Yonsei University at Seoul, South Korea in 2017. His Master’s thesis topic is’ Automated swept path analysis of oversized cargo transportation through deep reinforcement learning’. He is currently a member of NEXTLAB Co., Ltd. as an Assistant Research Engineer. His research interests include machine vision, data science, and artificial intelligence systems.

Soo-Hong Lee is currently as a Full-Time Professor at the Department of Mecha-nical Engineering, Yonsei University in Seoul, Korea. He received his Bachelor’s degree in Mechanical Engineering from Seoul National University in 1981 and his Master’s degree in Mechanical Engineering Design from Seoul National University in 1983. He completed his Ph.D. from Stanford University, California, USA, in 1991. His current research interests include intelligent CAD, knowledge-based engineering design, concurrent engineering, product design management, product lifecycle management, artificial intelligence in design, and design automation.

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Son, H., Lee, SH. Three-dimensional model retrieval in single category geometry using local ontology created by object part segmentation through deep neural network. J Mech Sci Technol 35, 5071–5079 (2021). https://doi.org/10.1007/s12206-021-1024-z

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  • DOI: https://doi.org/10.1007/s12206-021-1024-z

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