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Lightweight Web3D Visualization Framework Using Dijkstra-Based Mesh Segmentation

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E-Learning and Games (Edutainment 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10345))

Abstract

With the advent of the era of “Internet plus”, there are great achievement in Web3D technology areas, furthermore, more and more focuses have put on how to more effectively show dense models on browser. The paper proposes a framework to lightweight process the 3D shape based on Web Browser. This framework is based on Mesh Segmentation. Therefore, a new Dijkstra-based mesh segmentation approach is presented. The framework splits models and creates corresponding components, moreover, some repetitive components can be detected by our proposed framework. Firstly, a model barycenter is computed as a start point, besides, global distance is presented as the shortest path basis. Then, mesh triangles begin to diffuse until the conditions are not met. Secondly, according to the triangles diffuse, the original model will be re-indexed in order to acquire the segmentation files. Last but not least, repetition detection algorithm has been proposed, the components will be detected to confirm whether or not there exists the repetitive relationship of each other. In addition, experimental results on the Stanford and SHREC 2007 datasets show that our approach is accurate and feasible.

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References

  1. Kalogerakis, E., Chaudhuri, S., Koller, D., et al.: A probabilistic model for component-based shape synthesis. ACM Trans. Graph. 31(31), 1–11 (2012)

    Google Scholar 

  2. Theologou, P., Pratikakis, I., Theoharis, T.: A review on 3D object retrieval methodologies using a part-based representation. Comput. Aided Des. Appl. 11(6), 670–684 (2014)

    Article  Google Scholar 

  3. Savelonas, M.A., Pratikakis, I., Sfikas, K.: An overview of partial 3D object retrieval methodologies. Multimedia Tools Appl. 74(24), 11783–11808 (2015)

    Article  Google Scholar 

  4. Aleksey, G., Funkhouser, T.: Consistent segmentation of 3D models. Comput. Graph. 33(3), 262–269 (2009)

    Article  Google Scholar 

  5. Xu, K., Li, H., Zhang, H., et al.: Style-content separation by anisotropic part scales. ACM Trans. Graph. (TOG) 29(1), 184 (2010)

    Google Scholar 

  6. Kreavoy, V., Dan, J., Sheffer, A.: Model composition from interchangeable components. In: Pacific Conference on Computer Graphics and Applications, pp. 129–138 (2007)

    Google Scholar 

  7. Huang, Q., Koltun, V., Guibas, L.J., et al.: Joint shape segmentation with linear programming. In: International Conference on Computer Graphics and Interactive Techniques, vol. 30, No. 6 (2011)

    Google Scholar 

  8. Sidi, O., Kaick, O.V., Kleiman, Y., et al.: Unsupervised co-segmentation of a set of shapes via descriptor-space spectral clustering. ACM Trans. Graph. 30(6), 126:1–126:10 (2011)

    Article  Google Scholar 

  9. Meng, M., Xia, J., Luo, J., et al.: Unsupervised co-segmentation for 3D shapes using iterative multi-label optimization. Comput. Aided Des. 45(2), 312–320 (2013)

    Article  MathSciNet  Google Scholar 

  10. Hu, R., Fan, L., Liu, L.: Co-segmentation of 3D shapes via subspace clustering. In: Computer Graphics Forum, pp. 1703–1713. Blackwell Publishing Ltd. (2012)

    Google Scholar 

  11. Liu, X., et al.: Low-rank 3D mesh segmentation and labeling with structure guiding. Comput. Graph. 46, 99–109 (2015)

    Article  Google Scholar 

  12. Shikhare, D., Bhakar, S., Mudur, S.P.: Compression of large 3D engineering models using automatic discovery of repeating geometric features. In: Vision Modeling and Visualization Conference. Aka GmbH, pp. 233–240 (2001)

    Google Scholar 

  13. Cai, K., Wang, W., Chen, Z., et al.: Exploiting repeated patterns for efficient compression of massive models. In: International Conference on Virtual Reality Continuum and ITS Applications in Industry. ACM, pp. 145–150 (2009)

    Google Scholar 

  14. Wen, L., Jia, J., Liang, S., et al.: LPM: lightweight progressive meshes towards smooth transmission of Web3D media over internet. In: Virtual Reality Continuum and its Applications in Industry, pp. 95–103 (2014)

    Google Scholar 

  15. Kettner, L.: Using generic programming for “Designing a Data Structure for Polyhedral Surfaces”. Comput. Geom. 13(1), 65–90 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  16. Shamir, A.: Segmentation and shape extraction of 3D boundary meshes. In: State of the Art Report Eurographics (2006)

    Google Scholar 

  17. Agathos, A., Pratikakis, I., Perantonis, S., et al.: 3D mesh segmentation methodologies for CAD applications. Comput. Aided Des. Appl. 4(6), 827–841 (2007)

    Article  Google Scholar 

  18. Attene, M., Katz, S., Mortara, M., et al.: Mesh segmentation - a comparative study. In: IEEE International Conference on Shape Modeling and Applications, p. 7. DBLP (2006)

    Google Scholar 

  19. Shlafman, S., et al.: Metamorphosis of polyhedral surfaces using decomposition. Comput. Graph. Forum 21(3), 219–228 (2002)

    Article  Google Scholar 

  20. Katz, S., Tal, A.: Hierarchical mesh decomposition using fuzzy clustering and cuts. ACM Trans. Graph. 22(3), 954–961 (2003)

    Article  Google Scholar 

  21. Garland, M., Willmott, A., Heckbert, P.S.: Hierarchical face clustering on polygonal surfaces. In: Symposium on Interactive 3D Graphics, Si3d 2001, Chapel Hill, NY, USA, March, pp. 49–58. DBLP (2001)

    Google Scholar 

  22. Inoue, K., Itoh, T., Yamada, A., et al.: Face clustering of a large-scale CAD model for surface mesh generation. Comput. Aided Des. 33(3), 251–261 (2001)

    Article  Google Scholar 

  23. Lai, Y., Hu, S., Martin, R.R., et al.: Rapid and effective segmentation of 3D models using random walks. Comput. Aided Geom. Des. 26(6), 665–679 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  24. Katz, S., Leifman, G., Tal, A., et al.: Mesh segmentation using feature point and core extraction. Vis. Comput. 21(8), 649–658 (2005)

    Article  Google Scholar 

  25. Mortara, M., Patane, G., Spagnuolo, M., et al.: Plumber: a method for a multi-scale decomposition of 3D shapes into tubular primitives and bodies. Stat. Methods Appl. 339–344 (2004)

    Google Scholar 

  26. Liu, R., Zhang, H.: Segmentation of 3D meshes through spectral clustering. In: Pacific Conference on Computer Graphics and Applications, pp. 298–305 (2004)

    Google Scholar 

  27. Lin, H.S., Liao, H.M., Lin, J., et al.: Visual salience-guided mesh decomposition. IEEE Trans. Multimedia 9(1), 46–57 (2007)

    Article  Google Scholar 

  28. Theologou, P., Pratikakis, I., Theoharis, T., et al.: A comprehensive overview of methodologies and performance evaluation frameworks in 3D mesh Segmentation. Comput. Vis. Image Underst. 135, 49–82 (2015)

    Article  Google Scholar 

  29. Chen, X., Golovinskiy, A., Funkhouser, T.: A benchmark for 3D mesh segmentation. ACM Trans. Graph. 28(3), 1–12 (2009)

    Article  Google Scholar 

Download references

Acknowledgments

The authors appreciate the comments and suggestions of all anonymous reviewers, whose comments significantly improved this paper. This work is supported by The Key Research Projects of Central University of Basic Scientific Research Funds for Cross Cooperation (201510-02), Research Fund for the Doctoral Program of Higher Education of China (No. 2013007211-0035) and Key project in scientific and technological of Jilin Province in China (No. 20140204088GX).

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Zhou, W., Jia, J. (2017). Lightweight Web3D Visualization Framework Using Dijkstra-Based Mesh Segmentation. In: Tian, F., Gatzidis, C., El Rhalibi, A., Tang, W., Charles, F. (eds) E-Learning and Games. Edutainment 2017. Lecture Notes in Computer Science(), vol 10345. Springer, Cham. https://doi.org/10.1007/978-3-319-65849-0_15

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  • DOI: https://doi.org/10.1007/978-3-319-65849-0_15

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  • Online ISBN: 978-3-319-65849-0

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