An Error Estimation Framework for Many-Light Rendering
 Computer Graphics Forum (Proceedings of Pacific Graphics 2016), Vol. 35, No. 7, pp. 431-439

Kosuke Nabata1,  Kei Iwasaki1,  Yoshinori Dobashi2,  Tomoyuki Nishita3
 1Wakayama University/UEI Research,  2Hokkaido University/UEI Research,  5UEI Research/Hiroshima Shudo University

   
Abstract
The popularity of many-light rendering, which converts complex global illumination computations into a simple sum of the illumination from virtual point lights (VPLs), for predictive rendering has increased in recent years. A huge number of VPLs are usually required for predictive rendering at the cost of extensive computational time. While previous methods can achieve significant speedup by clustering VPLs, none of these previous methods can estimate the total errors due to clustering. This drawback imposes on users tedious trial and error processes to obtain rendered images with reliable accuracy. In this paper, we propose an error estimation framework for many-light rendering. Our method transforms VPL clustering into stratified sampling combined with confidence intervals, which enables the user to estimate the error due to clustering without the costly computing required to sum the illumination from all the VPLs. Our estimation framework is capable of handling arbitrary BRDFs and is accelerated by using visibility caching, both of which make our method more practical. The experimental results demonstrate that our method can estimate the error much more accurately than the previous clustering method.

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Citation
@article{doba2007,
    author  = {Kosuke Nabata and Kei Iwasaki and Yoshinori Dobashi and Tomoyuki Nishit},
    title   = {Mn Error Esitmation Framework for Many-Light Rendering}
    journal = {	Computer Graphics Forum (Proceedings of Pacific Graphics 2016)},
    year    = {2016},
    volume  = {35},
    number  = {7},
    pages   = {431-439}
}
Acknowledgement
This research was partially supported by JSPS KAKENHI, Grant-in-Aid for Scientific Research on Innovative Areas, Grant Number JP15H05924.