A new microflake model with microscopic self-shadowing for accurate volume downsampling

Abstract : Naïve linear methods for downsampling high-resolution microflake volumes often produce inaccurate appearance, especially when input voxels are very opaque. Preserving correct appearance at all resolutions requires taking into account masking-shadowing effects that occur between and inside dense input voxels. We introduce a new microflake model whose additional parameters characterize self-shadowing effects at a microscopic scale. We provide an anisotropic self-shadowing function and microflake distributions for which the scattering coefficients and the phase functions of our model have closed-form expressions. We use this model in a new downsampling approach in which scattering parameters are computed from local estimations of self-shadowing probabilities in the input volume. Unlike previous work, our method handles datasets with spatially varying scattering parameters, semi-transparent volumes and datasets with intricate silhouettes. We show that our method generates LoDs with correct transparency and consistent appearance through scales for a wide range of challenging datasets, allowing for huge memory savings and efficient distant rendering without loss of quality.
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Article dans une revue
Computer Graphics Forum, Wiley, In press, 37 (2), pp.1-11
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Contributeur : Guillaume Loubet <>
Soumis le : mardi 6 février 2018 - 14:03:02
Dernière modification le : jeudi 22 février 2018 - 10:38:52


  • HAL Id : hal-01702000, version 1



Guillaume Loubet, Fabrice Neyret. A new microflake model with microscopic self-shadowing for accurate volume downsampling. Computer Graphics Forum, Wiley, In press, 37 (2), pp.1-11. 〈hal-01702000〉



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