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Spectral caustic rendering of a homogeneous caustic object based on wavelength clustering and eye sensitivity

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An Erratum to this article was published on 25 November 2014

Abstract

In the real world, the index of refraction of a refractive object (caustic object) varies across the wavelengths. Therefore, in physically based caustic rendering, we need to take into account spectral information. However, this may lead to prohibitive running time. In response, we propose a two-step acceleration scheme for spectral caustic rendering. Our acceleration scheme takes into account information across visible wavelengths of the scene, that is, the index of refraction (IOR) (caustic object), light power (light), and material reflectance (surface). To process visible wavelengths effectively, firstly we cluster the wavelengths which have similar first refraction (air to caustic object) directions. In this way, all the wavelengths in a cluster can be represented by one light ray during rendering. Secondly, by considering the surrounding objects (their material reflectance from and visible surface area of the caustic objects) and light power, we compute the refinement amount of each wavelength cluster. Our accelerated algorithm can produce photorealistic rendering results close to their reference images (which are generated by rendering every 1 nm of visible wavelengths) with a significant acceleration magnitude. Computational experiment results and comparative analyses are reported in the paper.

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References

  1. Lai, G., Christensen, N.: A compression method for spectral photon map rendering. In: 15th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2007, WSCG’2007 - In Co-operation with EUROGRAPHICS, Full Papers ProceedingsWSCG Proceedings, pp. 95–102. Plzen (2007)

  2. Hachisuka, T.: Toshiya Hachisuka at UTokyo http://www.ci.i.u-tokyo.ac.jp/hachisuka/

  3. Hachisuka, T., Jensen, H.W.: Stochastic progressive photon mapping. ACM Trans. Graph. 28(5), 141:1–141:8 (2009)

  4. Useful color data. http://www.cis.rit.edu/research/mcsl2/online/cie.php

  5. Frisvad, J.R., Christensen, N.J., Jensen, H.W.: Computing the scattering properties of participating media using lorenz-mie theory. ACM Trans. Graph. 26(3) (2007). doi:10.1145/1276377.1276452

  6. Gutierrez, D., Seron, F.J., Munoz, A., Anson, O.: Visualizing underwater ocean optics. Comput. Graph. Forum 27(2), 547–556 (2008). doi:10.1111/j.1467-8659.2008.01152.x

    Article  Google Scholar 

  7. Guy, S., Soler, C.: Graphics gems revisited: fast and physically-based rendering of gemstones. ACM Trans. Graph. 23(3), 231–238 (2004). doi:10.1145/1015706.1015708

  8. Wright, W.D.: A re-determination of the trichromatic coefficients of the spectral colours. Trans. Opt. Soc. 30(4), 141 (1929)

    Article  Google Scholar 

  9. Jensen, H.W.: Global illumination using photon maps pp. 21–30 (1996)

  10. Johnson, G., Fairchild, M.: Full-spectral color calculations in realistic image synthesis. IEEE Comput. Graph. Appl. 19(4), 47–53 (1999)

    Article  Google Scholar 

  11. Radziszewski, M., Boryczko, K., Alda, W.: An improved technique for full spectral rendering. J. WSCG 17(1–3), 9–16 (2009)

    Google Scholar 

  12. Shi, J., Zhu, D., Zhang, Y., Wang, Z.: Realistically rendering polluted water. Vis. Comput. 28(6–8), 647–656 (2012). doi:10.1007/s00371-012-0685-0

    Article  Google Scholar 

  13. Dong, W.: Rendering optical effects based on spectra representation in complex scenes. In: Nishita, T., Peng, Q., Seidel, H.-P. (eds.) Advances in Computer Graphics. Lecture Notes in Computer Science, vol. 4035, pp. 719–726. Springer, Berlin Heidelbeg (2006)

  14. Gondek, J.S., Meyer, G.W., Newman, J.G.: Wavelength dependent reflectance functions. Proceedings of the 21st annual conference on Computer graphics and interactive techniques. SIGGRAPH ’94, pp. 213–220. ACM, New York, NY, USA (1994)

  15. Hirayama, H., Kaneda, K., Yamashita, H., Yamaji, Y., Monden, Y.: Visualization of optical phenomena caused by multilayer films with complex refractive indices. In: Computer graphics and applications, 1999. Proceedings. Seventh Pacific Conference on, pp. 128–137, 320 (1999)

  16. Sun, Y., Fracchia, F., Calvert, T., Drew, M.: Deriving spectra from colors and rendering light interference. Comput. Graph. Appl. IEEE 19(4), 61–67 (1999)

    Article  Google Scholar 

  17. Iwasaki, K., Matsuzawa, K., Nishita, T.: Real-time rendering of soap bubbles taking into account light interference. In: Proceedings of the Computer graphics international, pp. 344–348. IEEE Computer Society (2004)

  18. Durikovič, R., Kimura, R.: Spectrum-based rendering using programmable graphics hardware. In: Proceedings of the 21st spring conference on Computer graphics, pp. 233–236. ACM, Budmerice, Slovakia (2005)

  19. Durikovic, R., Kimura, R.: GPU rendering of the thin film on paints with full spectrum. In: Information visualization, 2006. IV 2006. Tenth International Conference on, pp. 751–756 (2006)

  20. Sun, Y.: Rendering biological iridescences with RGB-based renderers. ACM Trans. Graph. 25(1), 100–129 (2006)

    Article  Google Scholar 

  21. Sun, Y., Wang, Q.: Interference shaders of thin films. Comput. Graph. Forum 27, 1607–1631 (2008)

    Article  MATH  Google Scholar 

  22. Stam, J.: Diffraction shaders. In: Proceedings of the 26th annual conference on Computer graphics and interactive techniques, pp. 101–110. ACM Press/Addison-Wesley Publishing Co. (1999)

  23. Agu, E.O.: Diffraction shading models in computer graphics. Ph.D. thesis, University of Massachusetts Amherst (2001)

  24. Imura, M., Abe, T., Kanaya, I., Yasumuro, Y., Manabe, Y., Chihara, K.: Rendering of Play of color using stratified model based on amorphous structure of opal. Techniques and Applications, Proceedings VIIth Digital Image Computing (2003)

  25. Wu, J., Zheng, C., Hu, X., Xu, F.: Rendering realistic spectral bokeh due to lens stops and aberrations. Vis. Comput. 29(1), 41–52 (2013). doi:10.1007/s00371-012-0673-4

    Article  Google Scholar 

  26. Musgrave, F.K.: Prisms and rainbows: a dispersion model for computer graphics. Proc. Graph. Interface 89, 227–234 (1989)

    Google Scholar 

  27. Sadeghi, I., Munoz, A., Laven, P., Jarosz, W., Seron, F., Gutierrez, D., Jensen, H.W.: Physically-based simulation of rainbows. ACM Trans. Graph. 31(1), 3:13:12 (2012). doi:10.1145/2077341.2077344

  28. Xing, X., Dong, W., Zhang, X., Paul, J.C.: Spectrally-based single image relighting. In: Proceedings of the Entertainment for education and 5th international conference on E-learning and games, Edutainment’10, pp. 509–517. Springer, Berlin, Heidelberg (2010)

  29. Bergner, S., Mller, T., Drew, M.S., Finlayson, G.D.: Interactive spectral volume rendering. In: Proceedings of the conference on Visualization ’02, pp. 101–108. IEEE Computer Society, Boston, Massachusetts (2002)

  30. Strengert, M., Klein, T., Botchen, R., Stegmaier, S., Chen, M., Ertl, T.: Spectral volume rendering using GPU-based raycasting. Vis. Comput. 22(8), 550–561 (2006)

    Article  Google Scholar 

  31. Marimont, D.H., Wandell, B.A.: Linear models of surface and illuminant spectra. J. Opt. Soc. Am. A 9(11), 1905–1913 (1992)

    Article  Google Scholar 

  32. Chern, J.R., Wang, C.M.: A novel progressive refinement algorithm for full spectral rendering. Real Time Imaging 11(2), 117–127 (2005)

    Article  MathSciNet  Google Scholar 

  33. Rougeron, G., Proche, B., Lisse, C.S.: An adaptive representation of spectral data for reflectance computations. In: Eurographics Rendering Workshop, pp. 127–138. Springer (1997)

  34. Xu, H., Sun, Y.: Compact representation of spectral BRDFs using fourier transform and spherical harmonic expansion. Comput. Graph. Forum 25(4), 759–775 (2006)

    Article  Google Scholar 

  35. Zeghers, E., Carr, S., Bouatouch, K.: Error-bound wavelength selection for spectral rendering. Vis. Comput. 13(9–10), 424–434 (1998)

    Article  MATH  Google Scholar 

  36. Iehl, J., Proche, B.: Adaptive spectral rendering with a perceptual control. Comput. Graph. Forum 19(3), 291–299 (2000)

    Article  Google Scholar 

  37. Meyer, G.W.: Wavelength selection for synthetic image generation. Comput. Vis. Graph. Image Process. 41(1), 57–79 (1988)

    Article  Google Scholar 

  38. Deville, P.M., Merzouk, S., Cazier, D., Paul, J.C.: Spectral data modeling for a lighting application. Comput. Graph. Forum 13(3), 97–106 (1994)

    Article  Google Scholar 

  39. Sun, Y., Fracchia, F.D., Drew, M.S.: A composite spectral model and its applications. pp. 102–107 (2000)

  40. Sun, Y., Fracchia, F.D., Drew, M.S., Calvert, T.W.: A spectrally based framework for realistic image synthesis. Vis. Comput. 17(7), 429–444 (2001)

    Article  MATH  Google Scholar 

  41. Evans, G.F., McCool, M.D.: Stratified wavelength clusters for efficient spectral monte carlo rendering. In: Proceedings of the 1999 conference on Graphics interface ’99, p. 42–49. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999)

  42. Iehl, J., Proche, B.: Towards perceptual control of physically based spectral rendering. Comput. Graph. (Pergamon) 27(5), 747–762 (2003)

    Article  Google Scholar 

  43. Smits, B.: An RGB-to-spectrum conversion for reflectances. J. Graph. Tools 4(4), 11–22 (1999)

    Article  MathSciNet  Google Scholar 

  44. Wallis, R.: Fast computation of tristimulus values by use of gaussian quadrature. J. Opt. Soc. Am. 65(1), 91–94 (1975)

    Article  MathSciNet  Google Scholar 

  45. Boulos, S., Edwards, D., Lacewell, J.D., Kniss, J., Kautz, J., Shirley, P., Wald, I.: Packet-based whitted and distribution ray tracing. In: Proceedings of Graphics Interface 2007, GI ’07, p. 177–184. ACM, New York, NY, USA (2007). doi:10.1145/1268517.1268547

  46. Elek, O., Bauszat, P., Ritschel, T., Magnor, M., Seidel, H.P.: Spectral ray differentials. Computer Graphics Forum (Proceedings of EGSR) 33(4) (2014)

  47. Schjøth, L., Frisvad, J.R., Erleben, K., Sporring, J.: Photon differentials. In: Proceedings of the 5th international conference on Computer graphics and interactive techniques in Australia and Southeast Asia, pp. 179–186. ACM, Perth, Australia (2007). doi:10.1145/1321261.1321293

  48. Wyman, C., Davis, S.: Interactive image-space techniques for approximating caustics. In: Proceedings of the 2006 symposium on Interactive 3D graphics and games, pp. 153–160. ACM, Redwood City, California (2006). doi:10.1145/1111411.1111439

  49. Devlin, K., Chalmers, A., Wilkie, A., Purgathofer, W.: STAR: Tone reproduction and physically based spectral rendering. In: D. Fellner, R. Scopignio (eds.) State of the Art Reports, Eurographics 2002, pp. 101–123. The Eurographics Association (2002)

  50. Downloads | SCHOTT north america. http://www.us.schott.com/advanced_optics/english/download/index.html

  51. Weidlich, A., Wilkie, A.: Anomalous dispersion in predictive rendering. Comput. Graph. Forum 28(4), 1065–1072 (2009)

  52. NVIDIA OptiX ray tracing engine. http://developer.nvidia.com/optix

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Acknowledgments

This work is partially supported by a research grant MOE2011-T2-2-037 from Ministry of Education, Singapore. Henry Johan is supported by Fraunhofer IDM@NTU, which is funded by the National Research Foundation (NRF) and managed through the multiagency Interactive & Digital Media Programme Office (IDMPO) hosted by the Media Development Authority of Singapore (MDA).

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Authors and Affiliations

  1. gameLAB, School of Computer Engineering Organization (SCE), Nanyang Technological University (NTU), Block NS4, N4-B1b-13, 36 Nanyang Avenue, Singapore, 639798, Singapore

    Budianto Tandianus

  2. Fraunhofer IDM@NTU, Block NS1, N1-05-01, Nanyang Avenue, Singapore, 639798, Singapore

    Henry Johan

  3. School of Computer Engineering Organization (SCE), Nanyang Technological University (NTU), Block NS4, N4-02a-32, Nanyang Avenue, Singapore, 639798, Singapore

    Hock Soon Seah & Feng Lin

  4. School of Computer Engineering Organization (SCE), Nanyang Technological University (NTU), Block NS4, N4-02a-32, Nanyang Avenue, Singapore, 639798, Singapore

    Feng Lin

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  1. Budianto Tandianus
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Correspondence to Budianto Tandianus.

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Tandianus, B., Johan, H., Seah, H.S. et al. Spectral caustic rendering of a homogeneous caustic object based on wavelength clustering and eye sensitivity. Vis Comput 31, 1601–1614 (2015). https://doi.org/10.1007/s00371-014-1037-z

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