“Position-normal distributions for efficient rendering of specular microstructure”

  • ©Ling-Qi Yan, Milos Hasan, Steve Marschner, and Ravi Ramamoorthi

Conference:


Type:


Title:

    Position-normal distributions for efficient rendering of specular microstructure

Presenter(s)/Author(s):


Session Title: RENDERING OF COMPLEX MICROSTRUCTURE

Moderator(s):



Abstract:


    Specular BRDF rendering traditionally approximates surface microstructure using a smooth normal distribution, but this ignores glinty effects, easily observable in the real world. While modeling the actual surface microstructure is possible, the resulting rendering problem is prohibitively expensive. Recently, Yan et al. [2014] and Jakob et al. [2014] made progress on this problem, but their approaches are still expensive and lack full generality in their material and illumination support. We introduce an efficient and general method that can be easily integrated in a standard rendering system. We treat a specular surface as a four-dimensional position-normal distribution, and fit this distribution using millions of 4D Gaussians, which we call elements. This leads to closed-form solutions to the required BRDF evaluation and sampling queries, enabling the first practical solution to rendering specular microstructure.

References:


    1. Dong, Z., Walter, B., Marschner, S., and Greenberg, D. P. 2015. Predicting appearance from measured microgeometry of metal surfaces. ACM Trans. Graph. 35, 1. Google ScholarDigital Library
    2. Dunbar, D., and Humphreys, G. 2006. A spatial data structure for fast poisson-disk sample generation. ACM Trans. Graph. 25, 3. Google ScholarDigital Library
    3. Dupuy, J., Heitz, E., Iehl, J.-C., Poulin, P., Neyret, F., and Ostromoukhov, V. 2013. Linear Efficient Antialiased Displacement and Reflectance Mapping. ACM Trans. Graph. 32, 6. Google ScholarDigital Library
    4. Han, C., Sun, B., Ramamoorthi, R., and Grinspun, E. 2007. Frequency domain normal map filtering. ACM Trans. Graph. 26, 3. Google ScholarDigital Library
    5. Heitz, E. 2014. Understanding the masking-shadowing function in microfacet-based BRDFs. Journal of Computer Graphics Techniques (JCGT) 3, 2, 48–107.Google Scholar
    6. Igehy, H. 1999. Tracing ray differentials. SIGGRAPH 1999. Google ScholarDigital Library
    7. Jakob, W., Regg, C., and Jarosz, W. 2011. Progressive expectation–maximization for hierarchical volumetric photon mapping. Computer Graphics Forum (Proceedings of EGSR 2011) 30, 4. Google ScholarDigital Library
    8. Jakob, W., Hašan, M., Yan, L.-Q., Lawrence, J., Ramamoorthi, R., and Marschner, S. 2014. Discrete stochastic microfacet models. ACM Trans. Graph. 33, 4. Google ScholarDigital Library
    9. Jakob, W., 2010. Mitsuba renderer. http://www.mitsuba-renderer.org.Google Scholar
    10. Olano, M., and Baker, D. 2010. Lean mapping. ACM, I3D 2010, 181–188. Google ScholarDigital Library
    11. Toksvig, M. 2005. Mipmapping normal maps. Journal of Graphics Tools 10, 3, 65–71.Google ScholarCross Ref
    12. Tsai, Y.-T., and Shih, Z.-C. 2006. All-frequency precomputed radiance transfer using spherical radial basis functions and clustered tensor approximation. ACM Trans. Graph. 25, 3. Google ScholarDigital Library
    13. Veach, E. 1997. Robust Monte Carlo Methods for Light Transport Simulation. PhD thesis, Stanford University. Google ScholarDigital Library
    14. Verbeek, J., Nunnink, J., and Vlassis, N. 2006. Accelerated EM-based clustering of large data sets. Data Mining and Knowledge Discovery 13, 3, 291–307. Google ScholarDigital Library
    15. Xu, K., Sun, W.-L., Dong, Z., Zhao, D.-Y., Wu, R.-D., and Hu, S.-M. 2013. Anisotropic spherical gaussians. ACM Trans. Graph. 32, 6. Google ScholarDigital Library
    16. Yan, L.-Q., Hašan, M., Jakob, W., Lawrence, J., Marschner, S., and Ramamoorthi, R. 2014. Rendering glints on high-resolution normal-mapped specular surfaces. ACM Trans. Graph. 33, 4. Google ScholarDigital Library
    17. Zirr, T., and Kaplanyan, A. S. 2016. Real-time rendering of procedural multiscale materials. ACM, I3D 2016, 139–148. Google ScholarDigital Library


ACM Digital Library Publication: