Robust Statistical Pixel Estimation

Simple item page
dc.contributor.authorJung, Jin Wooen_US
dc.contributor.authorMeyer, Garyen_US
dc.contributor.authorDeLong, Ralphen_US
dc.contributor.editorOlga Sorkine-Hornung and Michael Wimmeren_US
dc.date.accessioned2015-04-16T07:46:08Z
dc.date.available2015-04-16T07:46:08Z
dc.date.issued2015en_US
dc.description.abstractRobust statistical methods are employed to reduce the noise in Monte Carlo ray tracing. Through the use of resampling, the sample mean distribution is determined for each pixel. Because this distribution is uni-modal and normal for a large sample size, robust estimates converge to the true mean of the pixel values. Compared to existing methods, less additional storage is required at each pixel because the sample mean distribution can be distilled down to a compact size, and fewer computations are necessary because the robust estimation process is sampling independent and needs a small input size to compute pixel values. The robust statistical pixel estimators are not only resistant to impulse noise, but they also remove general noise from fat-tailed distributions. A substantial speedup in rendering can therefore be achieved by reducing the number of samples required for a desired image quality. The effectiveness of the proposed approach is demonstrated for path tracing simulations.en_US
dc.description.number2en_US
dc.description.sectionheadersGlobal Illuminationen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume34en_US
dc.identifier.doi10.1111/cgf.12586en_US
dc.identifier.pages585-596en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12586en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectI.3.7 [Computer Graphics]en_US
dc.subjectThree Dimensional Graphics and Realismen_US
dc.subjectRaytracingen_US
dc.titleRobust Statistical Pixel Estimationen_US
Files
Collections
34-Issue 2
EG 2015 - Full Papers - CGF 34-Issue 2