Browsing by Author "Forbes, Angus G."

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    Temporally Stable Real-Time Joint Neural Denoising and Supersampling
    (ACM Association for Computing Machinery, 2022) Thomas, Manu Mathew; Liktor, Gabor; Peters, Christoph; Kim, Sungye; Vaidyanathan, Karthik; Forbes, Angus G.; Josef Spjut; Marc Stamminger; Victor Zordan
    Recent advances in ray tracing hardware bring real-time path tracing into reach, and ray traced soft shadows, glossy reflections, and diffuse global illumination are now common features in games. Nonetheless, ray budgets are still limited. This results in undersampling, which manifests as aliasing and noise. Prior work addresses these issues separately. While temporal supersampling methods based on neural networks have gained a wide use in modern games due to their better robustness, neural denoising remains challenging because of its higher computational cost. We introduce a novel neural network architecture for real-time rendering that combines supersampling and denoising, thus lowering the cost compared to two separate networks. This is achieved by sharing a single low-precision feature extractor with multiple higher-precision filter stages. To reduce cost further, our network takes low-resolution inputs and reconstructs a high-resolution denoised supersampled output. Our technique produces temporally stable high-fidelity results that significantly outperform state-of-the-art real-time statistical or analytical denoisers combined with TAA or neural upsampling to the target resolution. We introduce a novel neural network architecture for real-time rendering that combines supersampling and denoising, thus lowering the cost compared to two separate networks. This is achieved by sharing a single low-precision feature extractor with multiple higher-precision filter stages. To reduce cost further, our network takes low-resolution inputs and reconstructs a high-resolution denoised supersampled output. Our technique produces temporally stable high-fidelity results that significantly outperform state-of-the-art real-time statistical or analytical denoisers combined with TAA or neural upsampling to the target resolution.
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    Visualization in Astrophysics: Developing New Methods, Discovering Our Universe, and Educating the Earth
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Lan, Fangfei; Young, Michael; Anderson, Lauren; Ynnerman, Anders; Bock, Alexander; Borkin, Michelle A.; Forbes, Angus G.; Kollmeier, Juna A.; Wang, Bei; Smit, Noeska and Vrotsou, Katerina and Wang, Bei
    We present a state-of-the-art report on visualization in astrophysics. We survey representative papers from both astrophysics and visualization and provide a taxonomy of existing approaches based on data analysis tasks. The approaches are classified based on five categories: data wrangling, data exploration, feature identification, object reconstruction, as well as education and outreach. Our unique contribution is to combine the diverse viewpoints from both astronomers and visualization experts to identify challenges and opportunities for visualization in astrophysics. The main goal is to provide a reference point to bring modern data analysis and visualization techniques to the rich datasets in astrophysics.