Abstract
With the advent of the era of “big data”, increasing efforts have been focused on how to process large models to improve transmission over the internet and display in a browser, i.e., Web3D technology. Notwithstanding the many new advancements in Web3D technology, because browsers have limited storage capacity and low computational ability, the efficient display of a large model through the net remains a bottleneck problem. This paper proposes a light-weighting visualization framework, called the S-LPM framework, which includes a novel Dijkstra-based mesh segmentation operation and a new voxel-based repetition detection/removal operation to efficiently display large 3D models in a Web browser. The two key geometric operations substantially reduce the amount of data transmitted over the net, which in turn significantly increases the transmission speed. The partially transmitted data are then aligned through transformations to restore the entire original model and display it in the Web browser. The experimental results show that our approach is generally accurate and feasible, and its performance is superior to that of the benchmarking methods.




















Similar content being viewed by others
References
Agathos, A., Pratikakis, I., Perantonis, S., Sapidis, N., Azariadis, P.: 3D mesh segmentation methodologies for CAD applications [J]. Comput.-Aided Des. Applic. 4(6), 827–841 (2007)
Aleksey, G., Funkhouser, T.: Consistent segmentation of 3D models [J]. Comput. Graph. 33(3), 262–269 (2009)
Attene, M., Katz, S., Mortara, M., et al.: Mesh segmentation - a comparative study [C]. IEEE International Conference on Shape Modeling and Applications, pp. 7–7. DBLP (2006)
Cai, K., Wang, W., Chen, Z., et al.: Exploiting repeated patterns for efficient compression of massive models [C]. VRCIA, pp. 145–150. ACM (2009)
Cai, K., Teng, J., Teng, J., et al.: Exploiting repeated patterns for efficient compression of massive models [C]. International Conference on Virtual Reality Continuum and ITS Applications in Industry, pp. 145–150. ACM (2009)
Chen, X., Golovinskiy, A., Funkhouser, T.: A benchmark for 3D mesh segmentation [J]. ACM Trans. Graph. 28(3), 1–12 (2009)
Garland, M., Willmott, A., Heckbert, P.S.: Hierarchical face clustering on polygonal surfaces [C]. Symposium on Interactive 3d Graphics, Si3d 2001, Chapel Hill, Nc, Usa, March, pp. 49–58. DBLP (2001)
Hoppe, H.: Progressive meshes [J]. In: Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques [C], ACM SIGGRAPH ‘96, pp. 99–108 (1996)
Hu, R., Fan, L., Liu, L.: Co-segmentation of 3D shapes via subspace clustering [C]. Comput. Graphics Forum. Blackwell Publishing Ltd, pp. 1703–1713 (2012)
Huang, Q., Koltun, V., Guibas, L.J., et al.: Joint shape segmentation with linear programming [C]. International Conference on Computer Graphics and Interactive Techniques, 30(6) (2011)
Inoue, K., Itoh, T., Yamada, A., Furuhata, T., Shimada, K.: Face clustering of a large-scale CAD model for surface mesh generation [J]. Comput. Aided Des. 33(3), 251–261 (2001)
Isenburg, M., Lindstrom, P., Snoeyink, J.: Streaming compression of triangle meshes [C]. In Proceedings of the Third Eurographics Symposium on Geometry Processing (SGP '05) (2005)
Kalogerakis, E., Chaudhuri, S., Koller, D., et al.: A probabilistic model for component-based shape synthesis [J]. ACM Trans. Graph. 31(31), 1–11 (2012)
Katz, S., Tal, A.: Hierarchical mesh decomposition using fuzzy clustering and cuts [J]. ACM Trans. Graph. 22(3), 954–961 (2003)
Katz, S., Leifman, G., Tal, A., et al.: Mesh segmentation using feature point and core extraction [J]. Vis. Comput. 21(8), 649–658 (2005)
Kettner, L.: Using generic programming for “designing a data structure for polyhedral surfaces” [J]. Comput. Geom. 13(1), 65–90(26) (1999)
Kreavoy V, Julius D, Sheffer A. Model composition from interchangeable components [C]. Computer Graphics and Applications, 2007 PG'07. 15th Pacific Conference on. IEEE. 129–138 (2007)
Lai, Y., Hu, S., Martin, R., et al.: Rapid and effective segmentation of 3D models using random walks [J]. Comput. Aided Geom. Des. 26(6), 665–679 (2009)
Lin, H.S., Liao, H.M., Lin, J., et al.: Visual salience-guided mesh decomposition [J]. IEEE Trans. Multimedia. 9(1), 46–57 (2007)
Liu, R., Zhang, H.: Segmentation of 3D meshes through spectral clustering [C]. Pacific Conference on Computer Graphics and Applications, pp. 298–305 (2004)
Liu, X., et al.: Low-rank 3D mesh segmentation and labeling with structure guiding [J]. Comput Graph. 2015, 99–109 (2015)
Meng, M., Xia, J., Luo, J., He, Y.: Unsupervised co-segmentation for 3D shapes using iterative multi-label optimization [J]. Comput. Aided Des. 45(2), 312–320 (2013)
Mortara, M., Patane, G., Spagnuolo, M., et al.: Plumber: a method for a multi-scale decomposition of 3D shapes into tubular primitives and bodies [J]. JISS, pp. 339–344 (2004)
Saleem, M., Kamdar, M.R., Iqbal, A., Sampath, S., Deus, H.F., Ngonga Ngomo, A.C.: Big linked cancer data: integrating linked TCGA and PubMed [J]. Web Semantics Science Services & Agents on the World Wide Web. 27-28, 34–41 (2014)
Savelonas, M.A., Pratikakis, I., Sfikas, K.: An overview of partial 3D object retrieval methodologies [J]. Multimedia Tools and Applications. 74(24), 11783–11808 (2015)
Shamir, A.: Segmentation and shape extraction of 3D boundary meshes [C]. State of the Art Report Eurographics (2006)
Shikhare, D., Bhakar, S., Mudur, S.P.: Compression of large 3D engineering models using automatic discovery of repeating geometric features [C]. Vision Modeling and Visualization Conference, pp. 233–240. Aka GmbH (2001)
Shlafman, S., et al.: Metamorphosis of polyhedral surfaces using decomposition [J]. Comput.Graphics Forum. 21(3), 219–228 (2002)
Sidi O, Kaick O V, Kleiman Y, et al.: Unsupervised co-segmentation of a set of shapes via descriptor-space spectral clustering [J]. ACM Trans. Graph. 30(6), 1–10 (2011)
Theologou, P., Pratikakis, I., Theoharis, T.: A review on 3D object retrieval methodologies using a part-based representation [J]. Comput.-Aided Des. Applic. 11(6), 670–684 (2014)
Theologou, P., Pratikakis, I., Theoharis, T., et al.: A comprehensive overview of methodologies and performance evaluation frameworks in 3D mesh segmentation [J]. Comput. Vis. Image Underst. 135, 49–82 (2015)
Wen L., Xie N, Jia J. Fast accessing Web3D contents using lightweight progressive meshes [J]. Comput. Anim. Virtual Worlds. 27(5), 466–483 (2016)
Xu, K., Li, H., Zhang, H., et al.: Style-content separation by anisotropic part scales [J]. ACM Trans Graph. 29(1), 184 (2010)
Zhang, H., Gao, X., Wu, P., et al.: A cross-media distance metric learning framework based on multi-view correlation mining and matching [J]. Web Semantics Science Services & Agents on the World Wide Web. 19(2), 181–197 (2016)
Acknowledgments
The authors appreciate the comments and suggestions of all anonymous reviewers, whose comments helped significantly improve this paper. This work is supported by the Fundamental Research Funds for the Central Universities in China (2100219066) and the Key Fundamental Research Funds for the Central Universities in China (0200219153).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhou, W., Tang, K. & Jia, J. S-LPM: segmentation augmented light-weighting and progressive meshing for the interactive visualization of large man-made Web3D models. World Wide Web 21, 1425–1448 (2018). https://doi.org/10.1007/s11280-018-0610-1
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11280-018-0610-1