<div class="csl-bib-body">
<div class="csl-entry">Sakai, H., Freude, C., Wimmer, M., & Hahn, D. (2025). Statistical Error Reduction for Monte Carlo Rendering. In <i>Proceedings of the SIGGRAPH Asia 2025 Conference Papers</i> (pp. 1–12). ACM. https://doi.org/10.1145/3757377.3763995</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/222799
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dc.description.abstract
Denoising is an important post-processing step in physically based Monte Carlo (MC) rendering. While neural networks are widely used in practice, statistical analysis has recently become a viable alternative for denoising. In this paper, we present a general framework for statistics-based error reduction of both estimated radiance and variance. Specifically, we introduce a novel denoising approach for variance estimates, which can either improve variance-aware adaptive sampling or provide additional input for image denoising in a cascaded manner. Furthermore, we present multi-transform denoising: a general and efficient correction scheme for non-normal distributions, which typically occur in MC rendering. All these contributions combine to a robust denoising pipeline that does not require any pretraining and can run efficiently on current GPU hardware. Our results show distinct advantages over previous denoising methods, especially in the range of a few hundred samples per pixel, which is of high practical relevance. Finally, we demonstrate good convergence behavior as the number of samples increases, providing predictable results with low bias that are free of hallucinated neural artifacts. In summary, our statistics-based algorithms for adaptive sampling and denoising deliver fast, consistent, low-bias variance and radiance estimates.
en
dc.description.sponsorship
WWTF Wiener Wissenschafts-, Forschu und Technologiefonds
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dc.language.iso
en
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dc.subject
Monte Carlo rendering
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dc.subject
path tracing
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dc.subject
denoising
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dc.subject
image filtering
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dc.subject
statistics
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dc.title
Statistical Error Reduction for Monte Carlo Rendering
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dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.relation.isbn
979-8-4007-2137-3
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dc.relation.doi
10.1145/3757377
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dc.description.startpage
1
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dc.description.endpage
12
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dc.relation.grantno
10.47379/ICT22028
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
Proceedings of the SIGGRAPH Asia 2025 Conference Papers
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tuw.peerreviewed
true
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tuw.relation.publisher
ACM
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tuw.project.title
Toward Optimal Path Guiding for Photorealistic Rendering
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tuw.researchTopic.id
I5
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tuw.researchTopic.name
Visual Computing and Human-Centered Technology
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tuw.researchTopic.value
100
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tuw.publication.orgunit
E193-02 - Forschungsbereich Computer Graphics
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tuw.publication.orgunit
E057-16 - Fachbereich Center for Geometry and Computational Design