A Streaming Technology of 3D Design and Manufacturing Visualization Information Sharing for Cloud-Based Collaborative Systems

Part of the Springer Series in Advanced Manufacturing book series (SSAM)


One of the challenging problems that hinder the development of Cloud-based collaborative systems is the contradiction of large design or manufacturing visualization data and the limited bandwidth of the Internet and Web to share the data remotely to support collaborative work. Faster visualization of design and manufacturing models during collaboration has been needed for a long time. Recently, a new scheme for visualization has been presented, viz., the 3D streaming technology. 3D streaming technique can allow effective dispatch and access of large-volume design and manufacturing data as a series of patched streams across the Internet, and therefore provide a promising solution to overcome the obstacle. The key technology to realize the streaming technique is geometric simplification (or decimation) of 3D models. In this chapter, a new streaming technology based on a geometric simplification algorithm has been developed, in which two criteria are the crucial elements to control the collapse process for edges in 3D visualization models represented in VRML. After the simplification and sharing of a model, a developed refinement algorithm is carried out to restore the model from its simplified version back to its original, through combining the simplified model with some reconstruction data generated during the simplification process, therefore, to realize the streaming information sharing. The major feature of the streaming algorithm is that it has incorporated some advantages of the previously developed vertex decimation approach and edge collapse approach. Meanwhile, the mechanism of adaptive threshold parameters adopted in this work enhances the adaptability of the algorithm for various applications. Case studies and comparisons with some related works have been carried out to demonstrate the performance and potentials of the algorithm in terms of efficiency, adaptability and robustness.


Geometric Error Virtual Reality Modeling Language Vertex Pair Simple Vertex Corner Vertex 


  1. 1.
    Huang GQ, Mak KL (2001) Web-integrated manufacturing: recent developments and emerging issues. Int J Comput Integr Manuf 14(1):3–13CrossRefGoogle Scholar
  2. 2.
    Wang L, Shen W, Xie H, Neelamkavil J, Pardasani A (2002) Collaborative conceptual design: a state-of-the-art survey. Comput Aided Des 34(13):981–996CrossRefGoogle Scholar
  3. 3.
    Li WD, Lu WF, Fuh JYH, Wong YS (2005) Collaborative computer-aided design: research and development status. Comput Aided Des 37(5):931–940CrossRefGoogle Scholar
  4. 4.
    Schroeder WJ, Zarge JA, Lorensen E (1992) Decimation of triangle meshes. Comput Graph 26(2):65–70CrossRefGoogle Scholar
  5. 5.
    Franc M (2002) Methods for polygonal mesh simplification. Technical Report No. DCSE/TR-2002-01, University of West Bohemia in Pilsen. [Online Available: http://herakles.zcu.cz/~marty/html/rig/mpms.html ]
  6. 6.
    Soucy M, Laurendeau D (1996) Multi-resolution surface modeling based on hierarchical triangulation. Comput Vis Image Underst 63(1):1–14CrossRefGoogle Scholar
  7. 7.
    Garland M, Heckbert PS (1997) Surface simplification using quadric error metrics. In: Proceedings of SIGGRAPH’97, pp 209–216Google Scholar
  8. 8.
    Ronfard R, Rossignac J (1996) Full-range approximation of triangulated polyhedral. Comput Graph Forum 15(3):67–76CrossRefGoogle Scholar
  9. 9.
    Garland M, Heckbert PS (1998) Simplifying surfaces with color and texture using quadric error metrics. In: Proceedings of IEEE Visualization’98, pp 263–269Google Scholar
  10. 10.
    Hoppe H, DeRose T, Duchamp T, McDonald J, Stuetzle W (1993) Mesh optimization. In: Proceedings of SIGGRAPH’93, pp 19–26Google Scholar
  11. 11.
    Hoppe H (1996) Progressive meshes. In: Proceedings of SIGGRAPH’96, pp 99–108Google Scholar
  12. 12.
    Lindstrom P, Turk G (1998) Fast and memory efficient polygonal simplification. In: Proceedings of IEEE Visualization’98, pp 279–286Google Scholar
  13. 13.
    Cignoni P, Rocchini C, Scopigno R (1998) Metro: measuring error on simplified surface. Comput Graph Forum 17(2):167–174CrossRefGoogle Scholar
  14. 14.
    Garthwaite T, Reposa J (2000) Mesh decimation. Major Qualifying Project Report, MOW-2733, Worcester Polytechnic InstituteGoogle Scholar
  15. 15.
    SMF simple model format version 1.2. [Online Available: http://www.math.iastate.edu/burkardt/data/smf/smf.txt]
  16. 16.
    Renze KJ, Oliver JH (1996) Generalized unstructured decimation. IEEE Comput Graphics Appl 16(6):24–32CrossRefGoogle Scholar
  17. 17.
    Hamann B (1994) A data reduction scheme for triangulated surfaces. Comput Aided Geom Des 11:197–214MathSciNetMATHCrossRefGoogle Scholar
  18. 18.
    Cohen J, Varshney A, Manocha D, Turk G, Weber H, Agarwal P, Brooks F, Wright W (1996) Simplification envelopes. In: Proceedings of SIGGRAPH’96, pp 119–128Google Scholar
  19. 19.
    Qiu ZM, Wong Y, Fuh J, Chen Y, Zhou Z, Li WD, Lu Y (2004) Geometric model simplification for distributed CAD. Comput Aided Des 36(9):809–819CrossRefGoogle Scholar
  20. 20.
    Rossignac J, Borrel P (1993) Multi-resolution 3D approximation for rendering complex scenes. In: Falcidieno F, Kunii T (eds) Modeling in Computer Graphics: Methods and Applications, Springer-Verlag, Berlin Heidelberg, pp 455–465Google Scholar
  21. 21.
    Low KL, Tan TS (1997) Model simplification using vertex-clustering. In: Proceedings of SIGGRAPH’97, pp 75–81Google Scholar
  22. 22.
    Lindstrom P (2000) Out-of-core simplification of large polygonal models. In: Proceedings of SIGGRAPH’2000, pp 259–262Google Scholar
  23. 23.
    Cignoni P, Montani C, Scopigno R (1998) A comparison of mesh simplification algorithms. Comput Graph 22(1):37–54CrossRefGoogle Scholar
  24. 24.
    Luebke DP (2001) A developer’s survey of polygonal simplification algorithms. IEEE Comput Graphics Appl 21(3):24–35CrossRefGoogle Scholar
  25. 25.
    Chu CH, Chan YH, Wu PH (2008) 3D streaming based on multi-LOD models for networked collaborative design. Comput Ind 59(9):863–872CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Faculty of Engineering and ComputingCoventry UniversityCoventryUK
  2. 2.Singapore-MIT Alliance, Innovative Manufacturing System and Technology ProgramNational University of SingaporeSingaporeSingapore

Personalised recommendations