“Effects of global illumination approximations on material appearance” by Křivánek, Ferwerda and Bala

Rendering applications in design, manufacturing, ecommerce and other fields are used to simulate the appearance of objects and scenes. Fidelity with respect to appearance is often critical, and calculating global illumination (GI) is an important contributor to image fidelity; but it is expensive to compute. GI approximation methods, such as virtual point light (VPL) algorithms, are efficient, but they can induce image artifacts and distortions of object appearance. In this paper we systematically study the perceptual effects on image quality and material appearance of global illumination approximations made by VPL algorithms. In a series of psychophysical experiments we investigate the relationships between rendering parameters, object properties and image fidelity in a VPL renderer. Using the results of these experiments we analyze how VPL counts and energy clamping levels affect the visibility of image artifacts and distortions of material appearance, and show how object geometry and material properties modulate these effects. We find the ranges of these parameters that produce VPL renderings that are visually equivalent to reference renderings. Further we identify classes of shapes and materials that cannot be accurately rendered using VPL methods with limited resources. Using these findings we propose simple heuristics to guide visually equivalent and efficient rendering, and present a method for correcting energy losses in VPL renderings. This work provides a strong perceptual foundation for a popular and efficient class of GI algorithms.

1. Bartz, D., Cunningham, D., Fischer, J., Wallraven, C., and Brown, P. 2008. State-of-the-art of the role of perception for computer graphics. In Proc. Eurographics 2008, State of the Art Reports, 65–86.Google Scholar
2. Cole, F., Sanik, K., DeCarlo, D., Finkelstein, A., Funkhouser, T., Rusinkiewicz, S., and Singh, M. 2009. How well do line drawings depict shape? ACM Trans. Graph. 28, 3, 28:1–28:9. Google ScholarDigital Library
3. Daly, S. 1993. The visible differences predictor: an algorithm for the assessment of image fidelity. In Digital Images and Human Vision, A. B. Watson, Ed. MIT Press, 179–206. Google ScholarDigital Library
4. Debattista, K., Sundstedt, V., Santos, L. P., and Chalmers, A. 2005. Selective component-based rendering. In GRAPHITE 2005, 13–22. Google ScholarDigital Library
5. Dong, Z., Grosch, T., Ritschel, T., Kautz, J., and Seidel, H.-P. 2009. Real-time indirect illumination with clustered visibility. In Vision, Modeling, and Visualization Workshop 2009.Google Scholar
6. Dür, A. 2005. On the Ward model for global illumination. Unpublished manuscript, http://homepage.uibk.ac.at/~c70240/publications.html.Google Scholar
7. Dutré, P., Bala, K., and Bekaert, P. 2006. Advanced Global Illumination, 2nd Edition. A K Peters, Natick, MA. Google ScholarDigital Library
8. Fleming, R. W., and Bülthoff, H. H. 2005. Low-level image cues in the perception of translucent materials. ACM Trans. Appl. Percept. 2, 3, 346–382. Google ScholarDigital Library
9. Fleming, R. W., Dror, R. O., and Adelson, E. H. 2003. Real-world illumination and the perception of surface reflectance properties. Journal of Vision 3, 5, 347–368.Google ScholarCross Ref
10. Hašan, M., Pellacini, F., and Bala, K. 2007. Matrix row-column sampling for the many-light problem. ACM Trans. Graph. 26, 3, 26:1–26:10. Google ScholarDigital Library
11. Hašan, M., Křivánek, J., Walter, B., and Bala, K. 2009. Virtual spherical lights for many-light rendering of glossy scenes. ACM Trans. Graph. 28, 5, 143:1–143:6. Google ScholarDigital Library
12. Keller, A. 1997. Instant radiosity. In Proc. SIGGRAPH 97, 49–56. Google ScholarDigital Library
13. Khan, E. A., Reinhard, E., Fleming, R. W., and Bülthoff, H. H. 2006. Image-based material editing. ACM Trans. Graph. 25, 3, 654–663. Google ScholarDigital Library
14. Kollig, T., and Keller, A. 2004. Illumination in the presence of weak singularities. In Monte Carlo And Quasi-monte Carlo Methods, 245–257.Google Scholar
15. Laine, S., Saransaari, H., Kontkanen, J., Lehtinen, J., and Aila, T. 2007. Incremental instant radiosity for real-time indirect illumination. In Eurographics Symposium on Rendering, 277–286. Google ScholarDigital Library
16. Myszkowski, K. 2002. Perception-based global illumination, rendering, and animation techniques. In Spring Conference on Computer Graphics 2002, 13–24. Google ScholarDigital Library
17. Ngan, A., Durand, F., and Matusik, W. 2005. Experimental analysis of BRDF models. In Eurographics Symposium on Rendering, 117–126. Google ScholarDigital Library
18. O’Sullivan, C., Howlett, S., Morvan, Y., McDonnell, R., and O’Conor, K. 2004. Perceptually adaptive graphics. In Proc. Eurographics 2004, State of the Art Reports, 141–164.Google Scholar
19. Pellacini, F., Ferwerda, J. A., and Greenberg, D. P. 2000. Toward a psychophysically-based light reflection model for image synthesis. In Proc. SIGGRAPH 2000, 55–64. Google ScholarDigital Library
20. Ramanarayanan, G., Ferwerda, J., Walter, B., and Bala, K. 2007. Visual equivalence: towards a new standard for image fidelity. ACM Trans. Graph. 26, 3, 76:1–76:11. Google ScholarDigital Library
21. Ramanarayanan, G., Bala, K., and Ferwerda, J. A. 2008. Perception of complex aggregates. ACM Trans. Graph. 27, 3, 60:1–60:10. Google ScholarDigital Library
22. Reinhard, E., Stark, M., Shirley, P., and Ferwerda, J. 2002. Photographic tone reproduction for digital images. In Proc. SIGGRAPH 2002, 267–276. Google ScholarDigital Library
23. Ritschel, T., Grosch, T., Kim, M. H., Seidel, H.-P., Dachsbacher, C., and Kautz, J. 2008. Imperfect shadow maps for efficient computation of indirect illumination. ACM Trans. Graph. 27, 5, 129:1–129:8. Google ScholarDigital Library
24. Segovia, B., Iehl, J.-C., and Péroche, B. 2007. Metropolis instant radiosity. Computer Graphics Forum 26, 3, 425–434.Google ScholarCross Ref
25. Sloan, P.-P., Kautz, J., and Snyder, J. 2002. Precomputed radiance transfer for real-time rendering in dynamic, low-frequency lighting environments. In Proc. SIGGRAPH 2002, 527–536. Google ScholarDigital Library
26. Stokes, W. A., Ferwerda, J. A., Walter, B., and Greenberg, D. P. 2004. Perceptual illumination components: A new approach to efficient, high quality global illumination rendering. ACM Trans. Graph. 23, 3, 742–749. Google ScholarDigital Library
27. Vangorp, P., and Dutré, P. 2008. Shape-dependent gloss correction. In APGV ’08: Proceedings of the 5th Symposium on Applied Perception in Graphics and Visualization, 123–130. Google ScholarDigital Library
28. Vangorp, P., Laurijssen, J., and Dutré, P. 2007. The influence of shape on the perception of material reflectance. ACM Trans. Graph. 26, 3, 77:1–77:10. Google ScholarDigital Library
29. Wald, I., Kollig, T., Benthin, C., Keller, A., and Slusallek, P. 2002. Interactive global illumination using fast ray tracing. In Eurographics Workshop on Rendering, 15–24. Google ScholarDigital Library
30. Walter, B., Fernandez, S., Arbree, A., Bala, K., Donikian, M., and Greenberg, D. P. 2005. Lightcuts: a scalable approach to illumination. ACM Trans. Graph. 24, 3, 1098–1107. Google ScholarDigital Library
31. Walter, B., Arbree, A., Bala, K., and Greenberg, D. P. 2006. Multidimensional lightcuts. ACM Trans. Graph. 25, 3, 1081–1088. Google ScholarDigital Library
32. Ward, G. J. 1992. Measuring and modeling anisotropic reflection. In Proc. SIGGRAPH 92, 265–272. Google ScholarDigital Library
33. Westlund, H. B., and Meyer, G. W. 2001. Applying appearance standards to light reflection models. In Proc. SIGGRAPH 2001, 501–510. Google ScholarDigital Library
34. Yu, I., Cox, A., Kim, M. H., Ritschel, T., Grosch, T., Dachsbacher, C., and Kautz, J. 2009. Perceptual influence of approximate visibility in indirect illumination. ACM Trans. Appl. Percept. 6, 4, 24:1–24:14. Google ScholarDigital Library