Browsing by Author "Tsopouridis, Grigoris"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Deep and Fast Approximate Order Independent Transparency
    (© 2024 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2024) Tsopouridis, Grigoris; Vasilakis, Andreas A.; Fudos, Ioannis; Alliez, Pierre; Wimmer, Michael
    We present a machine learning approach for efficiently computing order independent transparency (OIT) by deploying a light weight neural network implemented fully on shaders. Our method is fast, requires a small constant amount of memory (depends only on the screen resolution and not on the number of triangles or transparent layers), is more accurate as compared to previous approximate methods, works for every scene without setup and is portable to all platforms running even with commodity GPUs. Our method requires a rendering pass to extract all features that are subsequently used to predict the overall OIT pixel colour with a pre‐trained neural network. We provide a comparative experimental evaluation and shader source code of all methods for reproduction of the experiments.
  • Thumbnail Image
    Item
    Traditional and Neural Order-Independent Transparency
    (The Eurographics Association, 2025) Tsopouridis, Grigoris; Georgiou-Mousses, Christos; Fudos, Ioannis; Corrigan, David; Franke, Tobias Alexander; Mantiuk, Rafal; Hildebrandt, Klaus
    Order independent transparency (OIT) is a technique in computer graphics that allows for accurate rendering of transparent objects without the need to sort them in a specific order based on their depth. Traditional transparency methods often suffer from artifacts and inaccuracies due to this sorting process, especially in complex scenes with many overlapping transparent surfaces. OIT is important because it provides a more visually correct representation of transparent materials, ensuring that colors mix accurately and that all elements are rendered consistently, regardless of their draw order. This enhances realism in applications such as video games, simulations, and visual effects in films. The tutorial will provide an overview of traditional (exact, approximate and hybrid) and deep learning approaches to OIT and examine their scope, performance and accuracy.