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Artistic Low Poly rendering for images

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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.

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References

  1. Bitencourt, B.: https://dribbble.com/Bitencourt (2014)

  2. Bhishek Aggarwal a.k.a AbhiKreationz. https://www.behance.net/abhikreationz (2014)

  3. Mordi, L.: https://www.behance.net/mdlv (2014)

  4. Image triangulator app: http://www.conceptfarm.ca/2013/portfolio/image-triangulator/ (2014)

  5. Trimaginator: https://itunes.apple.com/cn/app/trimaginator/id874969053 (2014)

  6. Art camera trigraff: https://itunes.apple.com/cn/app/art-camera-trigraff/id646603902 (2014)

  7. Kyprianidis, J., Collomosse, J., Wang, T., Isenberg, T.: A taxonomy of artistic stylization techniques for images and video, State of the’art’ (2012)

  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)

  9. Demaret, L., Dyn, N., Iske, A.: Image compression by linear splines over adaptive triangulations. Signal Process. 86(7), 1604–1616 (2006)

    Article  MATH  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  11. Adams, M.D.: A flexible content-adaptive mesh-generation strategy for image representation. Image Process. IEEE Trans. 20(9), 2414–2427 (2011)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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)

  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)

  16. Hausner, A.: Simulating decorative mosaics. In: Proceedings of the 28th annual conference on Computer graphics and interactive techniques, pp. 573–580. ACM (2001)

  17. Chen, Z., Xiao, Y., Cao, J.: Approximation by piecewise polynomials on voronoi tessellation. Graphical Models (2014)

  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)

  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)

    Article  Google Scholar 

  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)

  21. Garland, M., Zhou, Y.: Quadric-based simplification in any dimension. ACM Trans. Graph. (TOG) 24(2), 209–239 (2005)

    Article  Google Scholar 

  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)

  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)

  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)

  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)

  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)

    Article  Google Scholar 

  27. Zang, Y., Huang, H., Li, C.-F.: Artistic preprocessing for painterly rendering and image stylization. Vis. Comp. pp. 1–11 (2013)

  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)

    MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

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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.

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Correspondence to Meng Gai.

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Gai, M., Wang, G. Artistic Low Poly rendering for images. Vis Comput 32, 491–500 (2016). https://doi.org/10.1007/s00371-015-1082-2

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