Interactive reconstruction of Monte Carlo image sequences using a recurrent denoising autoencoder
Author:
Affiliation:
1. University of Montreal and McGill University
2. NVIDIA
3. NVIDIA and Karlsruhe Institute of Technology
4. McGill University
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Graphics and Computer-Aided Design
Link
https://dl.acm.org/doi/pdf/10.1145/3072959.3073601
Reference69 articles.
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2. Sample-Based Manifold Filtering for Interactive Global Illumination and Depth of Field
3. Bauszat P. Eisemann M. and Magnor M. 2011. Guided Image Filtering for Interactive High-quality Global Illumination (Comp. Graph. Forum). 8. Bauszat P. Eisemann M. and Magnor M. 2011. Guided Image Filtering for Interactive High-quality Global Illumination (Comp. Graph. Forum). 8.
4. Bitterli B. Rousselle F. Moon B. Iglesias-Guitián J. A. Adler D. Mitchell K. Jarosz W. and Novák J. 2016. Nonlinearly Weighted First-order Regression for Denoising Monte Carlo Renderings. Comp. Graph. Forum 35 4 (2016). Bitterli B. Rousselle F. Moon B. Iglesias-Guitián J. A. Adler D. Mitchell K. Jarosz W. and Novák J. 2016. Nonlinearly Weighted First-order Regression for Denoising Monte Carlo Renderings. Comp. Graph. Forum 35 4 (2016).
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