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

Guillaume Loubet 1 Fabrice Neyret 1
1 MAVERICK - Models and Algorithms for Visualization and Rendering
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
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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Submitted on : Tuesday, February 6, 2018 - 2:03:02 PM
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Guillaume Loubet, Fabrice Neyret. A new microflake model with microscopic self-shadowing for accurate volume downsampling: Implementing the microscopic self-shadowing microflake model. Computer Graphics Forum, Wiley, 2018, 37 (2), pp.111-121. ⟨10.1111/cgf.13346⟩. ⟨hal-01702000⟩

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