BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:Australia/Brisbane
X-LIC-LOCATION:Australia/Brisbane
BEGIN:STANDARD
DTSTART:19920301T030000
TZOFFSETFROM:+1100
TZOFFSETTO:+1000
TZNAME:AEST
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20191203T080509Z
LOCATION:Plaza Meeting Room P1
DTSTART;TZID=Australia/Brisbane:20191120T110000
DTEND;TZID=Australia/Brisbane:20191120T112100
UID:siggraphasia_SIGGRAPH Asia 2019_sess118_papers_227@linklings.com
SUMMARY:A Differential Theory of Radiative Transfer
DESCRIPTION:Technical Papers\n\nA Differential Theory of Radiative Transfe
r\n\nZhang, Wu, Zheng, Gkioulekas, Ramamoorthi...\n\nPhysics-based differe
ntiable rendering is the task of estimating the derivatives of radiometric
measures with respect to scene parameters. The ability to compute these d
erivatives is necessary for enabling gradient-based optimization in a dive
rse array of applications: from solving analysis-by-synthesis problems to
training machine learning pipelines incorporating forward rendering proces
ses. Unfortunately, physics-based differentiable rendering remains challen
ging, due to the complex and typically nonlinear relation between pixel in
tensities and scene parameters.\n\nWe introduce a differential theory of r
adiative transfer, which shows how individual components of the radiative
transfer equation (RTE) can be differentiated with respect to arbitrary di
fferentiable changes of a scene. Our theory encompasses the same generalit
y as the standard RTE, allowing differentiation while accurately handling
a large range of light transport phenomena such as volumetric absorption a
nd scattering, anisotropic phase functions, and heterogeneity. To numerica
lly estimate the derivatives given by our theory, we introduce an unbiased
Monte Carlo estimator supporting arbitrary surface and volumetric configu
rations. Our technique differentiates path contributions symbolically and
uses additional boundary integrals to capture geometric discontinuities su
ch as visibility changes.\n\nWe validate our method by comparing our deriv
ative estimations to those generated using the finite-difference method. F
urthermore, we use a few synthetic examples inspired by real-world applica
tions in inverse rendering, non-line-of-sight (NLOS) and biomedical imagin
g, and design, to demonstrate the practical usefulness of our technique.\n
\nRegistration Category: Platinum Pass, Full Conference Pass, Full Confere
nce One-Day Pass
URL:https://sa2019.conference-program.com/presentation?id=papers_227&sess=
sess118
END:VEVENT
END:VCALENDAR