High-Performance Graphics 2018

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https://diglib.eg.org/handle/10.2312/2632676

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Browsing High-Performance Graphics 2018 by Subject "Antialiasing"

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    Correlation-Aware Semi-Analytic Visibility for Antialiased Rendering
    (ACM, 2018) Crassin, Cyril; Wyman, Chris; McGuire, Morgan; Lefohn, Aaron; Patney, Anjul and Niessner, Matthias
    Geometric aliasing is a persistent challenge for real-time rendering. Hardware multisampling remains limited to 8×, analytic coverage fails to capture correlated visibility samples, and spatial and temporal postfiltering primarily target edges of superpixel primitives. We describe a novel semi-analytic representation of coverage designed to make progress on geometric antialiasing for subpixel primitives and pixels containing many edges while handling correlated subpixel coverage. Although not yet fast enough to deploy, it crosses three critical thresholds: image quality comparable to 256× MSAA, faster than 64× MSAA, and constant space per pixel.
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    Detecting Aliasing Artifacts in Image Sequences Using Deep Neural Networks
    (ACM, 2018) Patney, Anjul; Lefohn, Aaron; Patney, Anjul and Niessner, Matthias
    In this short paper we present a machine learning approach to detect visual artifacts in rendered image sequences. Specifically, we train a deep neural network using example aliased and antialiased image sequences exported from a real-time renderer. The trained network learns to identify and locate aliasing artifacts in an input sequence, without comparing it against a ground truth. Thus, it is useful as a fully automated tool for evaluating image quality. We demonstrate the effectiveness of our approach in detecting aliasing in several rendered sequences. The trained network correctly predicts aliasing in 64×64×4 animated sequences with more than 90% accuracy for images it hasn't seen before. The output of our network is a single scalar between 0 and 1, which is usable as a quality metric for aliasing. It follows the same trend as (1-SSIM) for images with increasing sample counts.