Now showing items 1-2 of 2

    • Deep Compositional Denoising for High-quality Monte Carlo Rendering 

      Zhang, Xianyao; Manzi, Marco; Vogels, Thijs; Dahlberg, Henrik; Gross, Markus; Papas, Marios (The Eurographics Association and John Wiley & Sons Ltd., 2021)
      We propose a deep-learning method for automatically decomposing noisy Monte Carlo renderings into components that kernelpredicting denoisers can denoise more effectively. In our model, a neural decomposition module learns ...
    • Sampling Clear Sky Models using Truncated Gaussian Mixtures 

      Vitsas, Nick; Vardis, Konstantinos; Papaioannou, Georgios (The Eurographics Association, 2021)
      Parametric clear sky models are often represented by simple analytic expressions that can efficiently generate plausible, natural radiance maps of the sky, taking into account expensive and hard to simulate atmospheric ...