IEEE Trans Vis Comput Graph - Filtering Non-Linear Transfer Functions on Surfaces.

Tópicos

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Resumo

Applying non-linear transfer functions and look-up tables to procedural functions (such as noise), surface attributes, or even surface geometry are common strategies used to enhance visual detail. As with any textured or geometric detail, proper filtering is needed to reduce aliasing when viewed across a range of distances, but accurate and efficient transfer function filtering remains an open problem for several reasons: transfer functions are complex and non-linear, especially when mapped through procedural noise and/or geometry-dependent functions. We accurately solve this problem by computing and sampling from specialized filtering distributions on the fly, yielding very fast performance. We investigate the case where the transfer function to filter is a color map applied to surface textures, as well as color maps applied according to (microscale) geometric details. We introduce a novel representation of a (potentially modulated) color map's distribution over pixel footprints using Gaussian statistics and, in the more complex case of high-resolution color mapped microsurface details, our filtering is view- and light-dependent, and capable of correctly handling masking and occlusion effects. Our approach can be generalized to filter other physical-based rendering quantities. Our framework is also compatible with the case of transfer functions used to warp surface geometry.

Resumo Limpo

appli nonlinear transfer function lookup tabl procedur function nois surfac attribut even surfac geometri common strategi use enhanc visual detail textur geometr detail proper filter need reduc alias view across rang distanc accur effici transfer function filter remain open problem sever reason transfer function complex nonlinear especi map procedur nois andor geometrydepend function accur solv problem comput sampl special filter distribut fli yield fast perform investig case transfer function filter color map appli surfac textur well color map appli accord microscal geometr detail introduc novel represent potenti modul color map distribut pixel footprint use gaussian statist complex case highresolut color map microsurfac detail filter view lightdepend capabl correct handl mask occlus effect approach can general filter physicalbas render quantiti framework also compat case transfer function use warp surfac geometri

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