CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Surface Decimation by Scene Contents

by Pascual Castello
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 162 - Number 11
Year of Publication: 2017
Authors: Pascual Castello
10.5120/ijca2017913400

Pascual Castello . Surface Decimation by Scene Contents. International Journal of Computer Applications. 162, 11 ( Mar 2017), 1-8. DOI=10.5120/ijca2017913400

@article{ 10.5120/ijca2017913400,
author = { Pascual Castello },
title = { Surface Decimation by Scene Contents },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2017 },
volume = { 162 },
number = { 11 },
month = { Mar },
year = { 2017 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume162/number11/27284-2017913400/ },
doi = { 10.5120/ijca2017913400 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:08:42.949214+05:30
%A Pascual Castello
%T Surface Decimation by Scene Contents
%J International Journal of Computer Applications
%@ 0975-8887
%V 162
%N 11
%P 1-8
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Today, there is a wide variety of devices ranging from PC’s, game consoles, up to smartphones and tablets. These computing devices have major differences in performance and make mesh decimation still active in the field of research. One of the latest topics in the area has been to create simplification algorithms considering visual similarity. However, the full potential of most visual simplification algorithms has yet to be tapped, especially in soft real-time interactive computer simulations such as video games and virtual reality environments. In this paper, a new framework, in which occlusion and visibility are exploited intensively, is introduced in order to simplify models more accurately by taking into account their context in actual 3D scenes. Static background elements are simplified by considering the effect of their surroundings, decreasing the polygon count in the surfaces partially hidden by others. In addition, by allowing users to perform an optimal placement of the cameras in the scene, simplification in regions not seen from such viewpoints is dramatically increased. Dynamic elements, such as characters, accomplish a higher level of simplification since these elements which often consist of multiple meshes, for example, clothes, those resulting from the design stage. These meshes usually cover some regions of the base mesh and are used as occluders in order to increase the amount of polygon reduction in dynamic elements, barely losing image quality.

References
  1. C. And´ujar, C. Saona-V´azquez, I. Navazo, and P. Brunet. Integrating occlusion culling and levels of detail through hardly-visible sets. Computer Graphics Forum, 19(3):499–506, 2000.
  2. P. Castell´o, M. Chover, M. Sbert, and M. Feixas. Reducing complexity in polygonal meshes with view-based saliency. Computer Aided Geometric Design, 31(6):279–293, 2014. P. Castell´o, M. Sbert, M. Chover, and M. Feixas. Viewpoint-based simplification using f-divergences. Information Sciences, 178(11):2375–2388, 2008.
  3. P. Castell´o, M. Sbert, M. Chover, and M. Feixas. Viewpoint-driven simplification using mutual information. Computer Graphics, 32(4):451–463, 2008.
  4. P. Cignoni, C. Montani, and R. Scopigno. A comparison of mesh simplification algorithms. Computer Graphics, 22(1):37–54, 1998.
  5. T. M. Cover and J. A. Thomas. Elements of information theory. Wiley-Interscience, NY, USA, 1991.
  6. J. El-Sana, N. Sokolovsky, and T. C. Silva. Integrating occlusion culling with view-dependent rendering. In Proc. of the conference on Visualization ’01 (VIS ’01), pages 371–378, Washington DC, USA, 2001. IEEE Computer Society.
  7. M. Feixas, M. Sbert, and F. Gonz´alez. A unified information-theoretic framework for viewpoint selection and mesh saliency. ACM Transactions on Applied Perception, 6(1):1–23, 2009.
  8. M. Garland. Multiresolution modeling: Survey & future opportunities. In State of the Art Reports of EUROGRAPHICS ’99, pages 111–131, 1999.
  9. M. Garland and P. S. Heckbert. Surface simplification using quadric error metrics. In SIGGRAPH ’97: Proceedings of the 24th annual conference on Computer graphics and interactive techniques, pages 209–216. Addison-Wesley Publishing Co, ACM Press, 1997.
  10. P. Gerasimov. Omnidirectional shadow mapping. In R. Fernando, editor, GPU Gems: Programming Techniques, Tips, and Tricks for Real-Time Graphics, volume 20, chapter 12, pages 193–204. Addison-Wesley, 2004.
  11. E. Gobbetti and F. Marton. Far voxels: a multiresolution framework for interactive rendering of huge complex 3d models on commodity graphics platforms. ACM Transactions on Graphics, 24(3):878–885, 2005.
  12. A. Grundh¨ofer, B. Brombach, R. Scheibe, and B. Fr¨ohlich. Level of detail based occlusion culling for dynamic scenes. In Proc. of the 3rd international conference on Computer graphics and interactive techniques in Australasia and South East Asia (GRAPHITE ’05), pages 37–45, NY, USA, 2005. ACM Press.
  13. H. Hoppe, T. Derose, T. Duchamp, J. Mcdonald, and W. Stuetzle. Mesh optimization. In SIGGRAPH ’93: Proceedings of the 20th annual conference on Computer graphics and interactive techniques, pages 19–26, 1993.
  14. M. Isenburg, P. Lindstrom, S. Gumhold, and J. Snoeyink. Large mesh simplification using processing sequences. In VIS ’03: Proceedings of the 14th IEEE Visualization, pages 465–472, Washington DC, USA, 2003. IEEE Computer Society.
  15. B. S. Jong, J. L. Tseng, and W. H. Yang. An efficient and low-error mesh simplification method based on torsion detection. Visual Computer, 22(1):56–67, 2006.
  16. Y. Kho and M. Garland. User-guided simplification. In SI3D ’03: Proceedings of the symposium on Interactive 3D graphics, pages 123–126, NY, USA, 2003. ACM Press.
  17. C. H. Lee, A. Varshney, and D. W. Jacobs. Mesh saliency. In SIGGRAPH ’05: ACM SIGGRAPH 2005 Papers, pages 659–666, NY, USA, 2005. ACM Press.
  18. P. Lindstrom. Out-of-core simplification of large polygonal models. In SIGGRAPH ’00: Proceedings of the 27th annual conference on Computer graphics and interactive techniques, pages 259–262. Addison-Wesley Publishing Co, ACM Press, 2000.
  19. P. Lindstrom and G. Turk. Image-driven simplification. ACM Transactions on Graphics, 19(3):204–241, 2000.
  20. D. Luebke. A developer’s survey of polygonal simplification algorithms. IEEE Computer Graphics and Applications, 21(3):24–35, 2001.
  21. D. Luebke and B. Hallen. Perceptually-driven simplification for interactive rendering. In Proc. of the 12th EUROGRAPHICS Workshop on Rendering Techniques, pages 223–234, London, UK, 2001. Springer-Verlag.
  22. D. Luebke, M. Reddy, J. D. Cohen, A. Varshney, B. Watson, and R. Huebner. Level of detail for 3d graphics. Elsevier Science, San Francisco, USA, 2003.
  23. D. M. Rouse and S. S. Hemami. Analyzing the role of visual structure in the recognition of natural image content with multi-scale ssim. In Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, volume 6806, page 38, 2008.
  24. O. M. van Kaick and H. Pedrini. A comparative evaluation of metrics for fast mesh simplification. Computer Graphics Forum, 25(14):197–210, 2006.
  25. P. P. V´azquez, M. Feixas, M. Sbert, and W. Heidrich. Viewpoint selection using viewpoint entropy. In VMV ’01: Proceedings of the Vision Modeling and Visualization Conference, pages 273–280. Aka GmbH, 2001.
  26. P. P. V´azquez, M. Feixas, M. Sbert, and A. Llobet. Realtime automatic selection of good molecular views. Computer Graphics, 30(1):98–110, 2006.
  27. I. Viola, M. Feixas, M. Sbert, and M. E. Gr¨oller. Importance-driven focus of attention. IEEE Transactions on Visualization and Computer Graphics, 12(5):933–940, 2006.
  28. H. T. Vo and S. P. Callahan. Streaming simplification of tetrahedral meshes. IEEE Transactions on Visualization and Computer Graphics, 13(1):145–155, 2007.
  29. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600–612, 2004.
  30. Z. Wang, E. P. Simoncelli, and A. C. Bovik. Multi-scale structural similarity for image quality assessment. In Proc. of IEEE Asilomar Conference on Signals, Systems and Computers, volume 2, pages 1398–1402, 2004.
  31. N. Williams, D. Luebke, J. D. Cohen, M. Kelley, and B. Schubert. Perceptually guided simplification of lit, textured meshes. In SI3D ’03: Proceedings of the symposium on Interactive 3D graphics, pages 113–121, NY, USA, 2003. ACM Press.
  32. J. Wu and L. Kobbelt. A stream algorithm for the decimation of massive meshes. In Proc. of Graphics Interface, pages 185–192, 2003.
  33. Y. Wu, Y. He, and H. Cai. Qem-based mesh simplification with global geometry features preserved. In GRAPHITE ’04: Proceedings of the 2nd international conference on Computer graphics and interactive techniques in Australasia and South East Asia, pages 50–57, NY, USA, 2004. ACM Press.
  34. S. Yoon, B. Salomon, R. Gayle, and D. Manocha. Quick-vdr: Interactive view-dependent rendering of massive models. In Proc. of the conference on Visualization ’04 (VIS ’04), pages 131–138,Washington DC, USA, 2004. IEEE Computer Society.
  35. E. Zhang and G. Turk. Visibility-guided simplification. In VIS ’02: Proceedings of the conference on Visualization, pages 267–274,Washington DC, USA, 2002. IEEE Computer Society.
Index Terms

Computer Science
Information Sciences

Keywords

User-assisted simplification information-theoretic measures viewpoint selection occlusion

Powered by PhDFocusTM