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The Visual Computer

, Volume 32, Issue 4, pp 491–500 | Cite as

Artistic Low Poly rendering for images

  • Meng Gai
  • Guoping Wang
Original Article

Abstract

This paper presents an automatic approach for generating low poly rendering of images, which is particularly popular in the recent art design community. Distinguishing from the traditional image triangulation methods for the sake of compression or vectorization, we propose some critical principles of such Low Poly rendering problem, and simulate the artists creation procedures straightforwardly. To produce the visual effects with clear boundaries, we constrain the vertices along the feature edges extracted from the input image. By employing the Voronoi diagram iteration guided by a feature flow field, the vertices in the result image well reflect the feature structure of the local shape. Moreover, with the salient region detection, we can achieve different mesh densities between the front object and the background. Some special color processing techniques are employed to make our result more artistic. Our method works well on a wide variety of images, no matter raster photographs or artificial images. Experiments show that our approach is able to generate satisfying results similar to the artwork created by professional artists.

Keywords

Low poly Non-photorealistic rendering Image stylization Image decomposition 

Notes

Acknowledgments

This research was supported by Grant Nos. 61421062, 61170205, 61232014, 61472010 from National Natural Science Foundation of China. Also was supported by Grant No. 2012AA011503 from The National Key Technology Research and Development Program of China.

References

  1. 1.
    Bitencourt, B.: https://dribbble.com/Bitencourt (2014)
  2. 2.
    Bhishek Aggarwal a.k.a AbhiKreationz. https://www.behance.net/abhikreationz (2014)
  3. 3.
  4. 4.
  5. 5.
  6. 6.
  7. 7.
    Kyprianidis, J., Collomosse, J., Wang, T., Isenberg, T.: A taxonomy of artistic stylization techniques for images and video, State of the’art’ (2012)Google Scholar
  8. 8.
    Demaret, L., Dyn, N., Floater, M.S., Iske, A.: Adaptive thinning for terrain modelling and image compression. In Advances in multiresolution for geometric modelling, pp. 319–338. Springer (2005)Google Scholar
  9. 9.
    Demaret, L., Dyn, N., Iske, A.: Image compression by linear splines over adaptive triangulations. Signal Process. 86(7), 1604–1616 (2006)CrossRefzbMATHGoogle Scholar
  10. 10.
    Yang, Y., Wernick, M.N., Brankov, J.G.: A fast approach for accurate content-adaptive mesh generation. Image Process. IEEE Trans. 12(8), 866–881 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Adams, M.D.: A flexible content-adaptive mesh-generation strategy for image representation. Image Process. IEEE Trans. 20(9), 2414–2427 (2011)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Liao, Z., Hoppe, H., Forsyth, D., Yu, Y.: A subdivision-based representation for vector image editing. Vis. Comp. Graph. IEEE Trans. 18(11), 1858–1867 (2012)CrossRefGoogle Scholar
  13. 13.
    Sun, J., Liang, L., Wen, F., Shum, H.-Y.: Image vectorization using optimized gradient meshes. In: ACM Transactions on Graphics (TOG), vol. 26, p. 11. ACM (2007)Google Scholar
  14. 14.
    Lai, Y.-K., Hu, S.-M., Martin, R.R.: Automatic and topology-preserving gradient mesh generation for image vectorization. In: ACM Transactions on Graphics (TOG), vol. 28, p. 85. ACM, (2009)Google Scholar
  15. 15.
    Lecot, G., Levy, B.: Ardeco: Automatic region detection and conversion. In Proceedings of the 17th Eurographics conference on Rendering Techniques, pp. 349–360. Eurographics Association (2006)Google Scholar
  16. 16.
    Hausner, A.: Simulating decorative mosaics. In: Proceedings of the 28th annual conference on Computer graphics and interactive techniques, pp. 573–580. ACM (2001)Google Scholar
  17. 17.
    Chen, Z., Xiao, Y., Cao, J.: Approximation by piecewise polynomials on voronoi tessellation. Graphical Models (2014)Google Scholar
  18. 18.
    Faustino, G.M., De Figueiredo, L.H.: Simple adaptive mosaic effects. In Computer Graphics and Image Processing, 2005. SIBGRAPI 2005. 18th Brazilian Symposium on, pp. 315–322. IEEE (2005)Google Scholar
  19. 19.
    Topal, C., Akinlar, C.: Edge drawing: a combined real-time edge and segment detector. J. Vis. Commun. Image Represent. 23(6), 862–872 (2012)CrossRefGoogle Scholar
  20. 20.
    Swaminarayan, S., Prasad, L.: Rapid automated polygonal image decomposition. In Applied Imagery and Pattern Recognition Workshop, 2006. AIPR 2006. 35th IEEE, pp. 28–28. IEEE (2006)Google Scholar
  21. 21.
    Garland, M., Zhou, Y.: Quadric-based simplification in any dimension. ACM Trans. Graph. (TOG) 24(2), 209–239 (2005)CrossRefGoogle Scholar
  22. 22.
    De Goes, F., Cohen-Steiner, D., Alliez, P., Desbrun, M.: An optimal transport approach to robust reconstruction and simplification of 2d shapes. In Computer Graphics Forum, vol. 30, pp. 1593–1602. Wiley Online Library (2011)Google Scholar
  23. 23.
    Hershberger, J., Snoeyink, J.: An o (n log n) implementation of the douglas-peucker algorithm for line simplification. In Proceedings of the tenth annual symposium on Computational geometry, pp. 383–384. ACM (1994)Google Scholar
  24. 24.
    Kim, D., Son, M., Lee, Y., Kang, H., Lee, S.: Feature-guided image stippling. In: Computer Graphics Forum, vol. 27, pp. 1209–1216. Wiley Online Library (2008)Google Scholar
  25. 25.
    Rong, G., Tan, T.-S.: Jump flooding in gpu with applications to voronoi diagram and distance transform. In: Proceedings of the 2006 symposium on Interactive 3D graphics and games, pp.109–116. ACM (2006)Google Scholar
  26. 26.
    Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. Pattern Anal. Mach. Intell. IEEE Trans. 33(5), 898–916 (2011)CrossRefGoogle Scholar
  27. 27.
    Zang, Y., Huang, H., Li, C.-F.: Artistic preprocessing for painterly rendering and image stylization. Vis. Comp. pp. 1–11 (2013)Google Scholar
  28. 28.
    Li, X.-Y., Gu, Y., Hu, S.-M., Martin, R.R.: Mixed-domain edge-aware image manipulation. IEEE Trans. Image Processi. Publ. IEEE Signal Process. Soc. 22(5), 1915–1925 (2013)MathSciNetGoogle Scholar
  29. 29.
    Huang, S.-S., Zhang, G.-X., Lai, Y.-K., Kopf, J., Cohen-Or, D., Shi-Min, H.: Parametric meta-filter modeling from a single example pair. Vis. Comp. 30(6–8), 673–684 (2014)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Beijing Engineering Research Center of Virtual Simulation and VisualizationPeking UniversityBeijingChina
  2. 2.State Key Lab of Mathematical Engineering and Advanced ComputingWuxiChina

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