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.
1. Bako S. Vogels T. McWilliams B. Meyer M. Novák J. Harvill A. Sen P. DeRose T. and Rousselle F. 2017. Kernel-Predicting Convolutional Networks for Denoising Monte Carlo Renderings. ACM Trans. Graph. (2017). Bako S. Vogels T. McWilliams B. Meyer M. Novák J. Harvill A. Sen P. DeRose T. and Rousselle F. 2017. Kernel-Predicting Convolutional Networks for Denoising Monte Carlo Renderings. ACM Trans. Graph. (2017).
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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