P-RPF: Pixel-based Random Parameter Filtering for Monte Carlo Rendering
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Filtering results of the dof-dragons scene using RPF and
our method with 8 and 32 samples per pixel (spp).
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Abstract
In this paper we propose Pixel-based Random Parameter Filtering (P-RPF) for effciently denoising images generated
from complex illuminations with a high sample count. We design various operations of our method to have time
complexity that is independent from the number of samples per pixel. We compute feature weights by measuring the
functional relationships between MC inputs and output in a sample basis. To accelerate this sample-basis process we
propose to use an upsampling method for feature weights. We have applied our method to a wide variety of models
with different rendering effects. Our method runs signi?cantly faster than the original RPF, while maintaining visually
pleasing and numerically similar results. Furthermore the performance gap between our method and RPF increases as
we have more samples per pixel. As a result, our method shows more visually pleasing and numerically better results
of RPF in an equal-time comparison.
Contents
Technical report (PDF, 141KB)
Hyosub Park, Bochang Moon, Soomin Kim, and Sung-Eui Yoon
KAIST Tech. Report, CS-TR-2013-382
October, 2013
Dept. of Computer Science
KAIST
373-1 Guseong-dong, Yuseong-gu, Daejeon, 305-701
South Korea
sglabkaist dot gmail dot com