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DTSTART:19820101T123000
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BEGIN:VEVENT
DTSTAMP:20201212T050119Z
LOCATION:Zoom Room 3
DTSTART;TZID=Asia/Singapore:20201212T124200
DTEND;TZID=Asia/Singapore:20201212T124800
UID:siggraphasia_SIGGRAPH Asia 2020_sess125_papers_239@linklings.com
SUMMARY:Slope-Space Integrals for Specular Next Event Estimation
DESCRIPTION:Technical Papers, Technical Papers Q&A\n\nSlope-Space Integral
s for Specular Next Event Estimation\n\nLoubet, Zeltner, Holzschuch, Jakob
\n\nMonte Carlo light transport simulations often lack robustness in scene
s containing specular or near-specular materials. Widely used uni- and bid
irectional sampling strategies tend to find light paths involving such mat
erials with insufficient probability, producing unusable images that are c
ontaminated by significant variance.\n\nThis article addresses the problem
of sampling a light path connecting two given scene points via a single s
pecular reflection or refraction, extending the range of scenes that can b
e robustly handled by unbiased path sampling techniques. Our technique ena
bles efficient rendering of challenging transport phenomena caused by such
paths, such as underwater caustics or caustics involving glossy metallic
objects.\n\nWe derive analytic expressions that predict the total radiance
due to a single reflective or refractive triangle with a microfacet BSDF
and we show that this reduces to the well known Lambert boundary integral
for irradiance. We subsequently show how this can be leveraged to efficien
tly sample connections on meshes comprised of vast numbers of triangles. O
ur derivation builds on the theory of off-center microfacets and involves
integrals in the space of surface slopes.\n\nOur approach straightforw
ardly applies to the related problem of rendering glints with high-resolut
ion normal maps describing specular microstructure. Our formulation allevi
ates problems raised by singularities in filtering integrals and enables a
generalization of previous work to perfectly specular materials. We also
extend previous work to the case of GGX distributions and introduce new te
chniques to improve accuracy and performance.\n\nRegistration Category: Ul
timate Supporter, Ultimate Attendee
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