43-Issue 2

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https://diglib.eg.org/handle/10.2312/3606944

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Browsing 43-Issue 2 by Subject "CCS Concepts: Computing methodologies -> Computer graphics"

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    An Empirically Derived Adjustable Model for Particle Size Distributions in Advection Fog
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Kolárová, Monika; Lachiver, Loïc; Wilkie, Alexander; Bermano, Amit H.; Kalogerakis, Evangelos
    Realistically modelled atmospheric phenomena are a long-standing research topic in rendering. While significant progress has been made in modelling clear skies and clouds, fog has often been simplified as a medium that is homogeneous throughout, or as a simple density gradient. However, these approximations neglect the characteristic variations real advection fog shows throughout its vertical span, and do not provide the particle distribution data needed for accurate rendering. Based on data from meteorological literature, we developed an analytical model that yields the distribution of particle size as a function of altitude within an advection fog layer. The thickness of the fog layer is an additional input parameter, so that fog layers of varying thickness can be realistically represented. We also demonstrate that based on Mie scattering, one can easily integrate this model into a Monte Carlo renderer. Our model is the first ever non-trivial volumetric model for advection fog that is based on real measurement data, and that contains all the components needed for inclusion in a modern renderer. The model is provided as open source component, and can serve as reference for rendering problems that involve fog layers.
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    Non-Euclidean Sliced Optimal Transport Sampling
    (The Eurographics Association and John Wiley & Sons Ltd., 2024) Genest, Baptiste; Courty, Nicolas; Coeurjolly, David; Bermano, Amit H.; Kalogerakis, Evangelos
    In machine learning and computer graphics, a fundamental task is the approximation of a probability density function through a well-dispersed collection of samples. Providing a formal metric for measuring the distance between probability measures on general spaces, Optimal Transport (OT) emerges as a pivotal theoretical framework within this context. However, the associated computational burden is prohibitive in most real-world scenarios. Leveraging the simple structure of OT in 1D, Sliced Optimal Transport (SOT) has appeared as an efficient alternative to generate samples in Euclidean spaces. This paper pushes the boundaries of SOT utilization in computational geometry problems by extending its application to sample densities residing on more diverse mathematical domains, including the spherical space Sd, the hyperbolic plane Hd, and the real projective plane Pd. Moreover, it ensures the quality of these samples by achieving a blue noise characteristic, regardless of the dimensionality involved. The robustness of our approach is highlighted through its application to various geometry processing tasks, such as the intrinsic blue noise sampling of meshes, as well as the sampling of directions and rotations. These applications collectively underscore the efficacy of our methodology.