40-Issue 4

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https://diglib.eg.org/handle/10.2312/2633074

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Browsing 40-Issue 4 by Subject "Rendering"

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    Moving Basis Decomposition for Precomputed Light Transport
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Silvennoinen, Ari; Sloan, Peter-Pike; Bousseau, Adrien and McGuire, Morgan
    We study the problem of efficient representation of potentially high-dimensional, spatially coherent signals in the context of precomputed light transport. We present a basis decomposition framework, Moving Basis Decomposition (MBD), that generalizes many existing basis expansion methods and enables high-performance, seamless reconstruction of compressed data. We develop an algorithm for solving large-scale MBD problems. We evaluate MBD against state-of-the-art in a series of controlled experiments and describe a real-world application, where MBD serves as the backbone of a scalable global illumination system powering multiple, current and upcoming 60Hz AAA-titles running on a wide range of hardware platforms.
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    Optimised Path Space Regularisation
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Weier, Philippe; Droske, Marc; Hanika, Johannes; Weidlich, Andrea; Vorba, JirĂ­; Bousseau, Adrien and McGuire, Morgan
    We present Optimised Path Space Regularisation (OPSR), a novel regularisation technique for forward path tracing algorithms. Our regularisation controls the amount of roughness added to materials depending on the type of sampled paths and trades a small error in the estimator for a drastic reduction of variance in difficult paths, including indirectly visible caustics. We formulate the problem as a joint bias-variance minimisation problem and use differentiable rendering to optimise our model. The learnt parameters generalise to a large variety of scenes irrespective of their geometric complexity. The regularisation added to the underlying light transport algorithm naturally allows us to handle the problem of near-specular and glossy path chains robustly. Our method consistently improves the convergence of path tracing estimators, including state-of-the-art path guiding techniques where it enables finding otherwise hard-to-sample paths and thus, in turn, can significantly speed up the learning of guiding distributions.