EG2018

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https://diglib.eg.org/handle/10.2312/2632158

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Browsing EG2018 by Subject "Computer graphics"

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    Deep Learning for Graphics
    (The Eurographics Association, 2018) Mitra, Niloy J.; Ritschel, Tobias; Kokkinos, Iasonas; Guerrero, Paul; Kim, Vladimir; Rematas, Konstantinos; Yumer, Ersin; Ritschel, Tobias and Telea, Alexandru
    In computer graphics, many traditional problems are now better handled by deep-learning based data-driven methods. In applications that operate on regular 2D domains, like image processing and computational photography, deep networks are state-of-the-art, beating dedicated hand-crafted methods by significant margins. More recently, other domains such as geometry processing, animation, video processing, and physical simulations have benefited from deep learning methods as well. The massive volume of research that has emerged in just a few years is often difficult to grasp for researchers new to this area. This tutorial gives an organized overview of core theory, practice, and graphics-related applications of deep learning.
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    Monte Carlo Methods for Volumetric Light Transport Simulation
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Novák, Jan; Georgiev, Iliyan; Hanika, Johannes; Jarosz, Wojciech; Hildebrandt, Klaus and Theobalt, Christian
    The wide adoption of path-tracing algorithms in high-end realistic rendering has stimulated many diverse research initiatives. In this paper we present a coherent survey of methods that utilize Monte Carlo integration for estimating light transport in scenes containing participating media. Our work complements the volume-rendering state-of-the-art report by Cerezo et al. [CPP 05]; we review publications accumulated since its publication over a decade ago, and include earlier methods that are key for building light transport paths in a stochastic manner. We begin by describing analog and non-analog procedures for freepath sampling and discuss various expected-value, collision, and track-length estimators for computing transmittance. We then review the various rendering algorithms that employ these as building blocks for path sampling. Special attention is devoted to null-collision methods that utilize fictitious matter to handle spatially varying densities; we import two ''next-flight'' estimators originally developed in nuclear sciences. Whenever possible, we draw connections between image-synthesis techniques and methods from particle physics and neutron transport to provide the reader with a broader context.