EG 2015 - STARs

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https://diglib.eg.org/handle/10.2312/12147

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Browsing EG 2015 - STARs by Subject "I.3.3 [Computer Graphics]"

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    Photorealistic Rendering of Mixed Reality Scenes
    (The Eurographics Association and John Wiley & Sons Ltd., 2015) Kronander, Joel; Banterle, Francesco; Gardner, Andrew; Miandji, Ehsan; Unger, Jonas; K. Hormann and O. Staadt
    Photo-realistic rendering of virtual objects into real scenes is one of the most important research prob- lems in computer graphics. Methods for capture and rendering of mixed reality scenes are driven by a large number of applications, ranging from augmented reality to visual e ects and product visualization. Recent developments in computer graphics, computer vision, and imaging technology have enabled a wide range of new mixed reality techniques including methods for advanced image based lighting, cap- turing spatially varying lighting conditions, and algorithms for seamlessly rendering virtual objects directly into photographs without explicit measurements of the scene lighting. This report gives an overview of the state-of-the-art in this eld, and presents a categorization and comparison of current methods. Our in-depth survey provides a tool for understanding the advantages and disadvantages of each method, and gives an overview of which technique is best suited to a speci c problem.
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    Recent Advances in Adaptive Sampling and Reconstruction for Monte Carlo Rendering
    (The Eurographics Association and John Wiley & Sons Ltd., 2015) Zwicker, Matthias; Jarosz, Wojciech; Lehtinen, Jaakko; Moon, Bochang; Ramamoorthi, Ravi; Rousselle, Fabrice; Sen, Pradeep; Soler, Cyril; Yoon, Sungeui E.; K. Hormann and O. Staadt
    Monte Carlo integration is firmly established as the basis for most practical realistic image synthesis algorithms because of its flexibility and generality. However, the visual quality of rendered images often suffers from estimator variance, which appears as visually distracting noise. Adaptive sampling and reconstruction algorithms reduce variance by controlling the sampling density and aggregating samples in a reconstruction step, possibly over large image regions. In this paper we survey recent advances in this area. We distinguish between “a priori” methods that analyze the light transport equations and derive sampling rates and reconstruction filters from this analysis, and “a posteriori” methods that apply statistical techniques to sets of samples to drive the adaptive sampling and reconstruction process. They typically estimate the errors of several reconstruction filters, and select the best filter locally to minimize error. We discuss advantages and disadvantages of recent state-of-the-art techniques, and provide visual and quantitative comparisons. Some of these techniques are proving useful in real-world applications, and we aim to provide an overview for practitioners and researchers to assess these approaches. In addition, we discuss directions for potential further improvements.