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Visual saliency guided textured model simplification

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Abstract

Mesh geometry can be used to model both object shape and details. If texture maps are involved, it is common to let mesh geometry mainly model object shapes and let the texture maps model the most object details, optimising data size and complexity of an object. To support efficient object rendering and transmission, model simplification can be applied to reduce the modelling data. However, existing methods do not well consider how object features are jointly represented by mesh geometry and texture maps, having problems in identifying and preserving important features for simplified objects. To address this, we propose a visual saliency detection method for simplifying textured 3D models. We produce good simplification results by jointly processing mesh geometry and texture map to produce a unified saliency map for identifying visually important object features. Results show that our method offers a better object rendering quality than existing methods.

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References

  1. Balmelli, L., Taubin, G., Bernardini, F.: Space-optimized texture maps. Comput. Graph. Forum 21, 411–420 (2002)

    Article  Google Scholar 

  2. Bulbul, A., Koca, C., Capin, T., Güdükbay, U.: Saliency for animated meshes with material properties. In: Proc. Applied Perception in Graphics and Visualization, pp. 81–88 (2010)

  3. Chen, X., Saparov, A., Pang, B., Funkhouser, T.: Schelling points on 3d surface meshes. ACM Trans. Graph. 31(4), 29 (2012)

    Article  Google Scholar 

  4. Cheng, I., Bischof, W.F.: A perceptual approach to texture scaling based on human computer interaction. Short Paper of Eurographics 2006, 1–6 (2006)

    Google Scholar 

  5. Connor, C., Egeth, H., Yantis, S.: Visual attention: bottom-up versus top-down. Curr. Biol. 14(19), 850–852 (2004)

    Article  Google Scholar 

  6. Corsini, M., Larabi, M., Lavoué, G., Petrík, O., Váša, L., Wang, K.: Perceptual metrics for static and dynamic triangle meshes. In: Eurographics 2012-State of the Art Reports, pp. 135–157. The Eurographics Association (2012)

  7. Feixas, M., Sbert, M., Gonzalez, F.: Information-theoretic framework for viewpoint selection and mesh saliency. ACM Trans. Appl. Percept. 6(1), 1–23 (2009)

    Article  Google Scholar 

  8. Fuchs, H., Golddfeather, J., Hultquist, J.: Fast spheres, shadows, textures, transparencies, and image enhancements in pixel-planes. In: Proc. Siggraph, pp. 109–116 (1985)

  9. Gal, R., Cohen-OR, D.: Salient geometric features for partial shape matching and similarity. ACM Trans. Graph. 25(1), 130–150 (2006)

    Article  Google Scholar 

  10. Garland, M., Heckbert, P.: Surface simplification using quadric error metric. In: Proc. ACM SIGGRAPH, pp. 209–215 (1997)

  11. Gonzlez, C., Castell, P., Chover, M., Sbert, M., Feixas, M., Gumbau, J.: Simplification method for textured polygonal meshes based on structural appearance. Signal Image Video Process. 7(3), 479–492 (2013). doi:10.1007/s11760-013-0450-5

    Article  Google Scholar 

  12. Hoppe, H.: Progressive meshes. In: Proc. ACM SIGGRAPH, pp. 99–108 (1996)

  13. Hunter, A., Cohen, J.D.: Uniform frequency images: Adding geometry to images to produce space-efficient textures. In: Proc. IEEE Visualization, pp. 243–250 (2000)

  14. Iourcha, K., Nayak, K., Zhou, H.: System and method for fixed-rate block-based image compression with inferred pixel values. US Patent 5,956,431, 21 Sep 1999

  15. Itti, L., Koch, C.: Feature combination strategies for saliency-based visual attention systems. J. Electronic Imaging 10(1), 161–169 (2001)

    Article  Google Scholar 

  16. Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)

    Article  Google Scholar 

  17. Kadir, T., Brady, M.: Saliency, scale and image description. Int. J. Comput. Vision 45, 83–105 (2001)

    Article  MATH  Google Scholar 

  18. Kim, Y., Varshney, A.: Saliency guided enhancement for volume visualization. IEEE Trans. Vis. Comput. Graphics 12(5), 925–932 (2006)

    Article  Google Scholar 

  19. Lavouë, G.: A local roughness measure for 3d meshes and its application to visual masking. ACM Trans. Appl. Percept. 5(4), 1–23 (2009)

    Article  Google Scholar 

  20. Lee, C.H., Varshney, A., Jacobs, D.W.: Mesh saliency. In: Proc. ACM SIGGRAPH, pp. 659–666 (2005)

  21. Leifman, G., Shtrom, E., Tal, A.: Surface regions of interest for viewpoint selection. In: Proc. IEEE computer vision and pattern recognition (CVPR), pp. 414–421 (2012)

  22. Li, F.W.B., Lau, R.W.H., Kilis, D., Li, L.W.F.: Game-on-demand: an online game engine based on geometry streaming. ACM Trans. Multimedia Comput. Commun. Appl. 7(3), 19:1–19:22 (2011)

    Article  Google Scholar 

  23. Lindstrom, P.: Model simplification using image and geometry-based metrics. Ph.D. thesis, Georgia Institute of Technology (2000)

  24. Liu, Y.S., Liu, M., D. Kihara, K.R.: Salient critical points for meshes. In: Proc. ACM Solid and Physical Modeling, pp. 277–282 (2007)

  25. Longhurst, P., Debattista, K., Chalmers, A.: A gpu based saliency map for high-fidelity selective rendering. In: Proc. Computer Graphics, Virtual Reality, Visualisation and Interaction in Africa, pp. 21–29 (2006)

  26. Luebke, D.P., Hallen, B.: Perceptually-driven simplification for interactive rendering. In: Proc. Eurographics Workshop on Rendering Techniques, pp. 223–234. Springer (2001)

  27. Martinez, J., Andujar, C.: Space-optimized texture atlases for 3d scenes with per-polygon textures. In: Proc. Pacific Graphics, pp. 14–23 (2010)

  28. Menze, N., Guthe, M.: Towards perceptual simplification of models with arbitrary materials. Comput. Graphics Forum 29(7), 2261–2270 (2011)

    Article  Google Scholar 

  29. Miao, Y., Feng, J.: Perceptual-saliency extremum lines for 3d shape illustration. Vis Comput. 26(6–8), 433–443 (2010)

    Article  Google Scholar 

  30. Miao, Y., Feng, J.R.P.: Visual saliency guided normal enhancement technique for 3d shape depiction. Comput. Graphics 35(3), 706–712 (2011)

    Article  Google Scholar 

  31. Montabone, S., Soto, A.: Human detection using a mobile platform and novel features derived from a visual saliency mechanism. Image Vis. Comput. 28, 391–402 (2009)

    Article  Google Scholar 

  32. Mozer, M.C., Sitton, M.: Computational modeling of spatial attention. Attention 9:341–393 (1998)

  33. Ponomarenko, N., Battisti, F., Egiazarian, K., Astola, J., Lukin, V.: Metrics performance comparsion for color image database. In: Proc. 4th intnational workshop on video processing and quality metric for consumer electronics, pp. 14–16 (2009)

  34. Qu, L.J., Meyer, G.W.: Human detection using a mobile platform and novel features derived from a visual saliency mechanism. Image Vis. Comput. 28(3), 391–402 (2008)

    Google Scholar 

  35. Sander, P.V., Snyder, J., Gortler, S.J., Hoppe, H.: Texture mapping progressive meshes. In: Proc. ACM SIGGRAPH, pp. 409–416 (2001)

  36. Schaefer, S., McPhail, T., Warren, J.: Image deformation using moving least squares. ACM Trans. Graphics (SIGGRAPH 2006) 25(3), 533–540 (2006)

    Article  Google Scholar 

  37. Song, R., Liu, Y., Martin, R.R., Rosin, P.L.: Mesh saliency via spectral processing. ACM Trans. Graphics 33(1), 6:1–6:17 (2014)

    Article  MATH  Google Scholar 

  38. Ström, J., Akenine-Möller, T.: ipackman: High-quality, low-complexity texture compression for mobile phones. In: Proc. the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware, HWWS ’05, pp. 63–70 (2005)

  39. Tang, Y., Zhang, H., Wang, Q., Bao, H.: Importance-driven texture encoding based on samples. In: Computer Graphics International, pp. 169–176 (2005)

  40. Taubin, G.: Estimating the tensor of curvature of a surface from a polyhedral approximation. In: Proc. IEEE International Conference on Computer Vision, pp. 902–907 (1995)

  41. Walther, D., Koch, C.: Modeling attention to salient proto-objects. Neural Netw. 19(9), 1395–1407 (2006)

    Article  MATH  Google Scholar 

  42. Wang, N., Simoncelli, E., Bovik, A.: Multiscale structural similarity for image quality assessment. In: Proc. the 37th IEEE Asilomar Conference on Signals, Systems and computers, pp. 1398–1402 (2003)

  43. Yang, B., Wang, X., Li, F.W.B.: Salient region of textured 3d model. In: Proc. Pacific Graphics, pp. 78–84 (2010)

  44. Yoon, D., Kim, K.J.: 3d game model and texture generation using interactive genetic algorithm. In: Proceedings of the Workshop at SIGGRAPH Asia, pp. 53–58 (2012)

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Acknowledgments

This work was partly supported by the National High Technology Research and Development Program of China (863 Program, Grant No. 2013AA013701), Zhejiang Province Natural Science Foundation for Distinguished Young Scientists (Grant No. LR12F02001), and National Natural Science Foundation of China (Grant Nos. 61170214, 61472363, 61170098), Zhejiang Province Natural Science Foundation (Grant No. Z1101340).

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Authors and Affiliations

  1. School of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou, China

    Bailin Yang, Xun Wang & Zhaoyi Jiang

  2. School of Engineering and Computing Sciences, University of Durham, Durham, UK

    Frederick W. B. Li

  3. College of Computer Science, University of Zhengzhou, Zhengzhou, China

    Mingliang Xu

  4. State Key Lab of Virtual Reality Technology and Systems, Beihang University, Beijing, China

    Xiaohui Liang

  5. School of Business, Hunan University, Changsha, China

    Yanhui Jiang

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  1. Bailin Yang
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  2. Frederick W. B. Li
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  3. Xun Wang
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  4. Mingliang Xu
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  5. Xiaohui Liang
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  6. Zhaoyi Jiang
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  7. Yanhui Jiang
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Correspondence to Yanhui Jiang.

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Yang, B., Li, F.W.B., Wang, X. et al. Visual saliency guided textured model simplification. Vis Comput 32, 1415–1432 (2016). https://doi.org/10.1007/s00371-015-1129-4

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