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TZID:Australia/Brisbane
X-LIC-LOCATION:Australia/Brisbane
BEGIN:STANDARD
DTSTART:19920301T030000
TZOFFSETFROM:+1100
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BEGIN:VEVENT
DTSTAMP:20191203T080637Z
LOCATION:Great Hall 1&2
DTSTART;TZID=Australia/Brisbane:20191117T193900
DTEND;TZID=Australia/Brisbane:20191117T194000
UID:siggraphasia_SIGGRAPH Asia 2019_sess222_papers_227@linklings.com
SUMMARY:A Differential Theory of Radiative Transfer
DESCRIPTION:Technical Papers Fast-Forward \n\nA Differential Theory of Rad
iative Transfer\n\nZhang, Wu, Zheng, Gkioulekas, Ramamoorthi...\n\nPhysics
-based differentiable rendering is the task of estimating the derivatives
of radiometric measures with respect to scene parameters. The ability to c
ompute these derivatives is necessary for enabling gradient-based optimiza
tion in a diverse array of applications: from solving analysis-by-synthesi
s problems to training machine learning pipelines incorporating forward re
ndering processes. Unfortunately, physics-based differentiable rendering r
emains challenging, due to the complex and typically nonlinear relation be
tween pixel intensities and scene parameters.\n\nWe introduce a differenti
al theory of radiative transfer, which shows how individual components of
the radiative transfer equation (RTE) can be differentiated with respect t
o arbitrary differentiable changes of a scene. Our theory encompasses the
same generality as the standard RTE, allowing differentiation while accura
tely handling a large range of light transport phenomena such as volumetri
c absorption and scattering, anisotropic phase functions, and heterogeneit
y. To numerically estimate the derivatives given by our theory, we introdu
ce an unbiased Monte Carlo estimator supporting arbitrary surface and volu
metric configurations. Our technique differentiates path contributions sym
bolically and uses additional boundary integrals to capture geometric disc
ontinuities such as visibility changes.\n\nWe validate our method by compa
ring our derivative estimations to those generated using the finite-differ
ence method. Furthermore, we use a few synthetic examples inspired by real
-world applications in inverse rendering, non-line-of-sight (NLOS) and bio
medical imaging, and design, to demonstrate the practical usefulness of ou
r technique.\n\nRegistration Category: Platinum Pass, Full Conference Pass
, Full Conference One-Day Pass, Basic Conference Pass, Student One-Day Pas
s, Exhibitor Pass
URL:https://sa2019.conference-program.com/presentation?id=papers_227&sess=
sess222
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