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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1109/TVCG.2020.3035516
|2 doi
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|a eng
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| 100 |
1 |
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|a Jonsson, Daniel
|e verfasserin
|4 aut
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| 245 |
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|a Direct Transmittance Estimation in Heterogeneous Participating Media Using Approximated Taylor Expansions
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|c 2022
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 27.05.2022
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Evaluating the transmittance between two points along a ray is a key component in solving the light transport through heterogeneous participating media and entails computing an intractable exponential of the integrated medium's extinction coefficient. While algorithms for estimating this transmittance exist, there is a lack of theoretical knowledge about their behaviour, which also prevent new theoretically sound algorithms from being developed. For this purpose, we introduce a new class of unbiased transmittance estimators based on random sampling or truncation of a Taylor expansion of the exponential function. In contrast to classical tracking algorithms, these estimators are non-analogous to the physical light transport process and directly sample the underlying extinction function without performing incremental advancement. We present several versions of the new class of estimators, based on either importance sampling or Russian roulette to provide finite unbiased estimators of the infinite Taylor series expansion. We also show that the well known ratio tracking algorithm can be seen as a special case of the new class of estimators. Lastly, we conduct performance evaluations on both the central processing unit (CPU) and the graphics processing unit (GPU), and the results demonstrate that the new algorithms outperform traditional algorithms for heterogeneous mediums
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|a Journal Article
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|a Kronander, Joel
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Unger, Jonas
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Schon, Thomas B
|e verfasserin
|4 aut
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| 700 |
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|a Wrenninge, Magnus
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g 28(2022), 7 vom: 03. Juli, Seite 2602-2614
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|g volume:28
|g year:2022
|g number:7
|g day:03
|g month:07
|g pages:2602-2614
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|u http://dx.doi.org/10.1109/TVCG.2020.3035516
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