skip to main content
10.1145/3013971.3013973acmconferencesArticle/Chapter ViewAbstractPublication PagessiggraphConference Proceedingsconference-collections
research-article

An inverse rendering approach for heterogeneous translucent materials

Published:03 December 2016Publication History

ABSTRACT

Since heterogeneous translucent materials, such as natural jades and marble, are complex hybrids of different materials, it is difficult to set precise optical parameters for subsurface scattering model as the material really has. In this paper, an inverse rendering approach is presented for heterogeneous translucent materials from a single input photograph. Given one photograph with an object of a certain heterogeneous translucent material, our approach can generate material distribution and estimate heterogeneous optical parameters to render images that look similar to the input photograph. We initialize material distribution using 3D Simplex Noise combined with Fractal Brownian Motion, and set color pattern of the noise using histogram matching method. The volume data with heterogeneous optical parameters is initialized based on the value of color pattern matched noise, and it is rendered in a certain lighting condition using Monte Carlo ray marching method. An iteration process is designed to approximate optical parameters to minimize the difference between rendering result and input photograph. Then the volume data with optimal heterogeneous optical parameters is obtained, which can be used for rendering any geometry model in different lighting conditions. Experimental results show that heterogeneous translucent objects can be rendered precisely similar to the material in the photograph with our approach.

References

  1. Acosta, M. R. G., Diaz, J. C. V., and Castro, N. S. 2014. An innovative image-processing model for rust detection using perlin noise to simulate oxide textures. Corrosion Science 88, 6, 141--151.Google ScholarGoogle ScholarCross RefCross Ref
  2. Adolfo, M., Echevarria, J. I., Seron, F. J., Jorge, L., Mashhuda, G., and Diego, G. 2011. Bssrdf estimation from single images. In Computer Graphics Forum, 455--464(10).Google ScholarGoogle Scholar
  3. Arbree, A., Walter, B., and Bala, K. 2010. Heterogeneous subsurface scattering using the finite element method. IEEE Transactions on Visualization and Computer Graphics 17, 7, 956--969. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Blasi, P., Sac, B. L., and Schlick, C. 1998. An Importance Driven Monte-Carlo Solution to the Global Illumination Problem. Springer Berlin Heidelberg.Google ScholarGoogle Scholar
  5. Blinn, J. F. 1982. Light reflection functions for simulation of clouds and dusty surfaces. Acm Siggraph Computer Graphics 16, 16, 21--29. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Dachsbacher, C., Krivanek, J., Hasan, M., Arbree, A., Walter, B., and Novak, J. 2014. Scalable realistic rendering with many-light methods. Computer Graphics Forum 33, 1, 88--104. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. D'Eon, E., and Irving, G. 2011. A quantized-diffusion model for rendering translucent materials. Acm Transactions on Graphics 30, 4, 1--14. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Dobashi, Y., Iwasaki, W., Ono, A., Yamamoto, T., Yue, Y., and Nishita, T. 2012. An inverse problem approach for automatically adjusting the parameters for rendering clouds using photographs. Acm Transactions on Graphics 31, 6, 439--445. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Ebert, D. S., and Parent, R. E. 1990. Rendering and animation of gaseous phenomena by combining fast volume and scanline a-buffer techniques. Acm Siggraph Computer Graphics 24, 4, 357--366. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Frisvad, J. R., Hachisuka, T., and Kjeldsen, T. K. 2014. Directional dipole model for subsurface scattering. Acm Transactions on Graphics 34, 1, 1--12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Gardner, G. Y. 1985. Visual simulation of clouds. Acm Siggraph Computer Graphics 19, 3, 297--304. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Gkioulekas, I., Zhao, S., Bala, K., Zickler, T., and Levin, A. 2013. Inverse volume rendering with material dictionaries. Acm Transactions on Graphics 32, 6, 1210--1214. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Gkioulekas, I., Xiao, B., Zhao, S., Adelson, E. H., Zickler, T., and Bala, K. 2014. Understanding the role of phase function in translucent appearance. Acm Transactions on Graphics 32, 32, 13--15. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Gustavson, S. 2005. Simplex noise demystified. Tne027 Digital Communication Electronics Lab2.Google ScholarGoogle Scholar
  15. Hachisuka, T., Jarosz, W., Georgiev, I., Kaplanyan, A., Nowrouzezahrai, D., and Spencer, B. 2012. State of the art in photon density estimation. In SIGGRAPH Asia, 1--562. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Heitz, E., Dupuy, J., Crassin, C., and Dachsbacher, C. 2015. The sggx microflake distribution. Acm Transactions on Graphics 34, 4, 1--11. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Hulst, V. D., and H., C. 1958. Book reviews: Light scattering by small particles. Science 127.Google ScholarGoogle Scholar
  18. Ishimaru, A., and Ishimaru, A. 1978. Wave propagation and scattering in random media. repr. of the 1978 orig. Wave Propagation and Scattering in Random Media 1, 6, 407--460.Google ScholarGoogle ScholarCross RefCross Ref
  19. Jakob, W., Arbree, A., Moon, J. T., Bala, K., and Marschner, S. 2010. A radiative transfer framework for rendering materials with anisotropic structure. Acm Transactions on Graphics 29, 4, 157--166. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Jakob, W., 2010. Mitsuba renderer.Google ScholarGoogle Scholar
  21. Jensen, H. W., Marschner, S. R., Levoy, M., and Hanrahan, P. 2002. A practical model for subsurface light transport. In Proceedings of the 28th annual conference on Computer graphics and interactive techniques, 511--518. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Kawai, J. K., Painter, J. S., and Cohen, M. F. 2010. Radioptimization - goal based rendering. In Computer Graphics, Conference Series, 147--154. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Klassen, R. V. 1987. Modeling the effect of the atmosphere on light. Acm Transactions on Graphics 6, 3, 215--237. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Lafortune, E. P., and Willems, Y. D. 1996. Rendering participating media with bidirectional path tracing. Eurographics 96, 91--100. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Li, D., Sun, X., Ren, Z., Lin, S., Tong, Y., Guo, B., and Zhou, K. 2013. Transcut: Interactive rendering of translucent cutouts. IEEE Transactions on Visualization and Computer Graphics 19, 3, 484--494. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Max, N. L. 1986. Atmospheric illumination and shadows. Acm Siggraph Computer Graphics 20, 4, 117--124. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Mishchenko, M. L., Travis, L. D., and Lacis, A. A. 2006. Multiple scattering of light by particles. In Multiple Scattering of Light by Particles.Google ScholarGoogle Scholar
  28. Mukaigawa, Y., Suzuki, K., and Yagi, Y. 2009. Analysis of subsurface scattering based on dipole approximation. Ipsj Transactions on Computer Vision and Applications 1, 1, 128--138.Google ScholarGoogle ScholarCross RefCross Ref
  29. Nishita, T. 1987. A shading model for atmospheric scattering considering luminous intensity distribution of light sources. Acm Siggraph Computer Graphics 21, 4, 303--310. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Papas, M., Regg, C., Jarosz, W., Bickel, B., Jackson, P., Matusik, W., Marschner, S., and Gross, M. 2013. Fabricating translucent materials using continuous pigment mixtures. Acm Transactions on Graphics 32, 4, 96--96. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Pattanaik, S. N., and Mudur, S. P. 1993. Computation of global illumination in a participating medium by monte carlo simulation. Journal of Visualization and Computer Animation 4, 3, 133--152.Google ScholarGoogle ScholarCross RefCross Ref
  32. Pellacini, F., Battaglia, F., Morley, R. K., and Finkelstein, A. 2007. Lighting with paint. In AMC Transaction on Graphics, 9. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Perlin, K. 1985. An image synthesizer. Acm Siggraph Computer Graphics 19, 3, 287--296. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Perlin, K., 2001. Noise hardware.Google ScholarGoogle Scholar
  35. Philippe, B., Bertr, S., and Christophe, S. 1993. A rendering algorithm for discrete volume density objects. In Computer Graphics Forum, 201--210.Google ScholarGoogle Scholar
  36. Sakas, G. 1990. Fast rendering of arbitrary distributed volume densities. In Eurographics.Google ScholarGoogle Scholar
  37. Sakas, G. 1993. Modeling and animating turbulent gaseous phenomena using spectral synthesis. Visual Computer 9, 9, 200--212. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Schoeneman, C., Dorsey, J., Smits, B., Arvo, J., and Greenberg, D. 1993. Painting with light. In Conference on Computer Graphics and Interactive Techniques, SIGGRAPH, 143--146. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Siegel, R., Howell, J. R., and Siegel, R. 1992. Thermal radiation heat transfer - third edition. Bristol, PA (United States); Hemisphere Publishing.Google ScholarGoogle Scholar
  40. Stam, J. 1995. Multiple scattering as a diffusion process. Springer Vienna.Google ScholarGoogle Scholar
  41. Stam, J. 1996. Multi-scale stochastic modelling of complex natural phenomena. Thesis Department of Computer Science University of Toronto. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Wang, J., Zhao, S., Tong, X., Lin, S., Lin, Z., Dong, Y., Guo, B., and Shum, H. Y. 2008. Modeling and rendering of heterogeneous translucent materials using the diffusion equation. Acm Transactions on Graphics Tog Homepage 27, 1, 329--339. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Wang, J., Tong, X., Lin, S. S., Guo, B., Shum, H. Y., and Lin, Z., 2012. Modeling and rendering of heterogeneous translucent materials using the diffusion equation.Google ScholarGoogle Scholar
  44. Yaeger, L., Upson, C., and Myers, R. 2011. R.: Combining physical and visual simulation-creation of the planet jupiter for the film 2010. Acm Siggraph Computer Graphics 150, 6, 347--8. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Zhao, S., Ramamoorthi, R., and Bala, K. 2014. High-order similarity relations in radiative transfer. Acm Transactions on Graphics 33, 4, 1--12. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. An inverse rendering approach for heterogeneous translucent materials

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      VRCAI '16: Proceedings of the 15th ACM SIGGRAPH Conference on Virtual-Reality Continuum and Its Applications in Industry - Volume 1
      December 2016
      381 pages
      ISBN:9781450346924
      DOI:10.1145/3013971
      • Conference Chairs:
      • Yiyu Cai,
      • Daniel Thalmann

      Copyright © 2016 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 3 December 2016

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      Overall Acceptance Rate51of107submissions,48%

      Upcoming Conference

      SIGGRAPH '24

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader