“Vector solid textures” by Wang, Zhou, Yu and Guo

  • ©Lvdi Wang, Kun Zhou, Yizhou Yu, and Baining Guo




    Vector solid textures



    In this paper, we introduce a compact random-access vector representation for solid textures made of intermixed regions with relatively smooth internal color variations. It is feature-preserving and resolution-independent. In this representation, a texture volume is divided into multiple regions. Region boundaries are implicitly defined using a signed distance function. Color variations within the regions are represented using compactly supported radial basis functions (RBFs). With a spatial indexing structure, such RBFs enable efficient color evaluation during real-time solid texture mapping. Effective techniques have been developed for generating such a vector representation from bitmap solid textures. Data structures and techniques have also been developed to compactly store region labels and distance values for efficient random access during boundary and color evaluation.


    1. Chang, H.-H., and Hong, Y. 1998. Vectorization of hand-drawn image using piecewise cubic bézier curves fitting. Pattern recognition 31, 11, 1747–1755.Google Scholar
    2. Cohen-Steiner, D., Alliez, P., and Desbrun, M. 2004. Variational shape approximation. ACM Trans. Graph. 23, 3, 905–914. Google ScholarDigital Library
    3. Dong, Y., Lefebvre, S., Tong, X., and Drettakis, G. 2008. Lazy solid texture synthesis. Computer Graphics Forum 27, 4, 1165–1174.Google ScholarDigital Library
    4. Ebert, D. S., Musgrave, F. K., Peachey, D., Perlin, K., and Worley, S. 1994. Texturing and Modeling: A Procedural Approach. Academic Press. Google ScholarDigital Library
    5. Efros, A., and Leung, T. 1999. Texture synthesis by non-parametric sampling. In ICCV ’99, 1033–1038. Google ScholarDigital Library
    6. Frisken, S. F., Perry, R. N., Rockwood, A. P., and Jones, T. R. 2000. Adaptively sampled distance fields: a general representation of shape for computer graphics. In Proceedings of SIGGRAPH 2000, 249–254. Google ScholarDigital Library
    7. Ghazanfarpour, D., and Dischler, J.-M. 1995. Spectral analysis for automatic 3-D texture generation. Computers and Graphics 19, 3, 413–422.Google ScholarCross Ref
    8. Ghazanfarpour, D., and Dischler, J.-M. 1996. Generation of 3D texture using multiple 2D model analysis. Computer Graphics Forum 15, 3, 311–323.Google ScholarCross Ref
    9. Heeger, D., and Bergen, J. 1995. Pyramid-based texture analysis/synthesis. In Proceedings of SIGGRAPH ’95, 229–238. Google ScholarDigital Library
    10. Hertzmann, A., Jacobs, C. E., Oliver, N., Curless, B., and Salesin, D. H. 2001. Image analogies. In Proceedings of SIGGRAPH 2001, 327–340. Google ScholarDigital Library
    11. Hilaire, X., and Tombre, K. 2006. Robust and accurate vectorization of line drawings. IEEE Trans. Pattern Anal. Mach. Intell. 28, 6, 890–904. Google ScholarDigital Library
    12. Jagnow, R., Dorsey, J., and Rushmeier, H. 2004. Stereological techniques for solid textures. ACM Trans. Graph. 23, 3, 329–335. Google ScholarDigital Library
    13. Kopf, J., Fu, C.-W., Cohen-Or, D., Deussen, O., Lischinski, D., and Wong, T.-T. 2007. Solid texture synthesis from 2D exemplars. ACM Trans. Graph. 26, 3, Article 2. Google ScholarDigital Library
    14. Kwatra, V., Essa, I., Bobick, A., and Kwatra, N. 2005. Texture optimization for example-based synthesis. ACM Trans. Graph. 24, 3, 795–802. Google ScholarDigital Library
    15. Lagae, A., Lefebvre, S., Drettakis, G., and Dutré, P. 2009. Procedural noise using sparse Gabor convolution. ACM Trans. Graph. 28, 3, Article 54. Google ScholarDigital Library
    16. Lai, Y.-K., Hu, S.-M., and Martin, R. 2009. Automatic and topology-preserving gradient mesh generation for image vectorization. ACM Trans. Graph. 28, 3, Article 85. Google ScholarDigital Library
    17. Lecot, G., and Levy, B. 2006. Ardeco: Automatic region detection and conversion. In EGSR 2006, 349–360. Google ScholarDigital Library
    18. Lefebvre, S., and Hoppe, H. 2006. Appearance-space texture synthesis. ACM Trans. Graph. 25, 3, 541–548. Google ScholarDigital Library
    19. Lefebvre, S., Hornus, S., and Neyret, F. 2005. Octree textures on the GPU. In GPU Gems 2. ch. 37.Google Scholar
    20. Nehab, D., and Hoppe, H. 2008. Random-access rendering of general vector graphics. ACM Trans. Graph. 27, 5, Article 135. Google ScholarDigital Library
    21. Orzan, A., Bousseau, A., Winnemöller, H., Barla, P., Thollot, J., and Salesin, D. 2008. Diffusion curves: a vector representation for smooth-shaded images. ACM Trans. Graph. 27, 3, Article 92. Google ScholarDigital Library
    22. Parilov, E., and Zorin, D. 2008. Real-time rendering of textures with feature curves. ACM Trans. Graph. 27, 1, Article 3. Google ScholarDigital Library
    23. Price, B., and Barrett, W. 2006. Object-based vectorization for interactive image editing. The Visual Computer 22, 9 (sep), 661–670. Google ScholarDigital Library
    24. Qin, X., and Yang, Y.-H. 2007. Aura 3D textures. IEEE Transactions on Visualization and Computer Graphics 13, 2, 379–389. Google ScholarDigital Library
    25. Ramanarayanan, G., Bala, K., and Walter, B. 2004. Feature-based textures. In EGSR 2004, 65–73.Google Scholar
    26. Sen, P. 2004. Silhouette maps for improved texture magnification. In Graphics Hardware 2004, 65–73. Google ScholarDigital Library
    27. Sethian, J. 1999. Level Set Methods and Fast Marching Methods. Cambridge University Press.Google Scholar
    28. Sigg, C., and Hadwiger, M. 2005. Fast third-order texture filtering. In GPU Gems 2. ch. 20.Google Scholar
    29. Sun, J., Liang, L., Wen, F., and Shum, H.-Y. 2007. Image vectorization using optimized gradient meshes. ACM Trans. Graph. 26, 3, Article 11. Google ScholarDigital Library
    30. Takayama, K., Okabe, M., Ijiri, T., and Igarashi, T. 2008. Lapped solid textures: filling a model with anisotropic textures. ACM Trans. Graph. 27, 3, Article 53. Google ScholarDigital Library
    31. Tarini, M., and Cignoni, P. 2005. Pinchmaps: textures with customizable discontinuities. Computer Graphics Forum 24, 3, 557–568.Google ScholarCross Ref
    32. Tumblin, J., and Choudhury, P. 2004. Bixels: Picture samples with sharp embedded boundaries. In EGSR 2004, 186–196.Google Scholar
    33. Wei, L.-Y. 2001. Texture Synthesis by Fixed Neighborhood Searching. PhD thesis, Stanford University. Google ScholarDigital Library
    34. Welsh, D., and Powell, M. 1967. An upper bound for the chromatic number of a graph and its application to timetabling problems. The Computer Journal 10, 1, 85–86.Google ScholarCross Ref
    35. Worley, S. 1996. A cellular texture basis function. In Proceedings of SIGGRAPH ’96, 291–294. Google ScholarDigital Library
    36. Wyvill, G., McPheeters, C., and Wyvill, B. 1986. Data structure for soft objects. The Visual Computer 2, 4, 227–234.Google ScholarCross Ref
    37. Xia, T., Liao, B., and Yu, Y. 2009. Patch-based image vectorization with automatic curvilinear feature alignment. ACM Trans. Graph. 28, 5, Article 115. Google ScholarDigital Library
    38. Zhou, K., Ren, Z., Lin, S., Bao, H., Guo, B., and Shum, H.-Y. 2008. Real-time smoke rendering using compensated ray marching. ACM Trans. Graph. 27, 3, Article 36. Google ScholarDigital Library
    39. Zhu, C., Byrd, R., Lu, P., and Nocedal, J. 1997. L-BFGS-B: Fortran subroutines for large-scale bound constrained optimization. ACM Trans. Math. Softw. 23, 4, 550–560. Google ScholarDigital Library
    40. Zou, J. J., and Yan, H. 2001. Cartoon image vectorization based on shape subdivision. In Proceedings of Computer Graphics International 2001, 225–231. Google ScholarDigital Library

ACM Digital Library Publication:

Overview Page: