Denoising-Aware Adaptive Sampling for Monte Carlo Ray Tracing-Reference-Cited by-同舟云学术

Denoising-Aware Adaptive Sampling for Monte Carlo Ray Tracing

Published:2023-07-23 Issue: Volume: Page:
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Container-title:Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Proceedings
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Author:

Firmino Arthur¹^ORCID,Frisvad Jeppe Revall²^ORCID,Jensen Henrik Wann³^ORCID

Affiliation:

1. Luxion, Denmark and Technical University of Denmark, Denmark

2. Technical University of Denmark, Denmark

3. Luxion, United States of America

Funder

European Union Horizon 2020 Marie Sk?odowska-Curie Actions

Publisher

ACM

Reference58 articles.

1. A review of uncertainty quantification in deep learning: Techniques, applications and challenges

2. Attila T Áfra. 2019. Intel® Open Image Denoise. https://www.openimagedenoise.org/. Attila T Áfra. 2019. Intel® Open Image Denoise. https://www.openimagedenoise.org/.

3. The academy color encoding system (ACES): A professional color-management framework for production, post-production and archival of still and motion pictures;Arrighetti Walter;Journal of Imaging,2017

4. Deep combiner for independent and correlated pixel estimates;Back Jonghee;ACM Transactions on Graphics,2020

5. Jonghee Back , Binh-Son Hua , Toshiya Hachisuka , and Bochang Moon . 2022 . Self-supervised post-correction for Monte Carlo denoising . In SIGGRAPH 2022 Conference Papers. 18:1–18:8. https://doi.org/10 .1145/3528233.3530730 10.1145/3528233.3530730 Jonghee Back, Binh-Son Hua, Toshiya Hachisuka, and Bochang Moon. 2022. Self-supervised post-correction for Monte Carlo denoising. In SIGGRAPH 2022 Conference Papers. 18:1–18:8. https://doi.org/10.1145/3528233.3530730