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DTSTART:19820101T123000
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BEGIN:VEVENT
DTSTAMP:20201212T050119Z
LOCATION:Zoom Room 3
DTSTART;TZID=Asia/Singapore:20201212T124800
DTEND;TZID=Asia/Singapore:20201212T125400
UID:siggraphasia_SIGGRAPH Asia 2020_sess125_papers_268@linklings.com
SUMMARY:CPPM: Chi-squared Progressive Photon Mapping
DESCRIPTION:Technical Papers, Technical Papers Q&A\n\nCPPM: Chi-squared Pr
ogressive Photon Mapping\n\nLin, Li, Zeng, Zhang, Jia...\n\nWe present a n
ovel chi-squared progressive photon mapping algorithm (CPPM) that construc
ts an estimator by controlling the bandwidth to obtain superior image qual
ity. Our estimator has parametric statistical advantages over prior nonpar
ametric methods.\nFirst, we show that when a probability density function
of the photon distribution is subject to uniform distribution, the radianc
e estimation is theoretically unbiased.\nNext, the local photon distributi
on is evaluated via chi-squared test to determine whether the photons foll
ow the hypothesized distribution (uniform distribution) or not.\nIf the st
atistical test deems that the photon distribution inside the bandwidth per
mits unbiased estimation, bandwidth reduction should be suspended.\nFinall
y, we present a pipeline with a bandwidth retention and conditional reduct
ion scheme according to the test results. \nThis pipeline not only accumul
ates sufficient photons for a reliable chi-squared test but also theoretic
ally guarantees that the estimate converges to the correct solution.\nWe e
valuate our method on various benchmarks and observe significant improveme
nt in the running time and rendering quality in terms of mean squared erro
r over prior progressive photon mapping methods.\n\nRegistration Category:
Ultimate Supporter, Ultimate Attendee
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