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research article

Unbiased Inverse Volume Rendering with Differential Trackers

Nimier-David, Merlin  
•
Muller, Thomas
•
Keller, Alexander
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July 1, 2022
Acm Transactions On Graphics

Volumetric representations are popular in inverse rendering because they have a simple parameterization, are smoothly varying, and transparently handle topology changes. However, incorporating the full volumetric transport of light is costly and challenging, often leading practitioners to implement simplified models, such as purely emissive and absorbing volumes with "baked" lighting. One such challenge is the efficient estimation of the gradients of the volume's appearance with respect to its scattering and absorption parameters. We show that the straightforward approach-differentiating a volumetric free-flight sampler-can lead to biased and high-variance gradients, hindering optimization. Instead, we propose using a new sampling strategy: differential ratio tracking, which is unbiased, yields low-variance gradients, and runs in linear time. Differential ratio tracking combines ratio tracking and reservoir sampling to estimate gradients by sampling distances proportional to the unweighted transmittance rather than the usual extinction-weighted transmittance. In addition, we observe local minima when optimizing scattering parameters to reproduce dense volumes or surfaces. We showthat these local minima can be overcome by bootstrapping the optimization from nonphysical emissive volumes that are easily optimized.

  • Details
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Type
research article
DOI
10.1145/3528223.3530073
Web of Science ID

WOS:000830989200020

Author(s)
Nimier-David, Merlin  
•
Muller, Thomas
•
Keller, Alexander
•
Jakob, Wenzel  
Date Issued

2022-07-01

Publisher

ASSOC COMPUTING MACHINERY

Published in
Acm Transactions On Graphics
Volume

41

Issue

4

Start page

44

Subjects

Computer Science, Software Engineering

•

Computer Science

•

differentiable rendering

•

inverse rendering

•

volumetric rendering

•

radiative backpropagation

•

importance sampling

Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
RGL  
Available on Infoscience
September 12, 2022
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/190626
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