DocumentCode :
50400
Title :
Filtering Non-Linear TransferFunctions on Surfaces
Author :
Heitz, Eric ; Nowrouzezahrai, Derek ; Poulin, P. ; Neyret, Fabrice
Author_Institution :
INRIA-LJK, Univ. de Grenoble, St. Ismier, France
Volume :
20
Issue :
7
fYear :
2014
fDate :
Jul-14
Firstpage :
996
Lastpage :
1008
Abstract :
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. Their simplicity and ability to mimic a wide range of realistic appearances have led to their adoption in many rendering problems. 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, and the effects of perspective and masking further complicate the filtering over a pixel´s footprint. 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 (macroscale) surface textures (like noise), 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. We propose an application to shading with irradiance environment maps over large terrains. Our framework is also compatible with the case of transfer functions used to warp surface geometry, as long as the transformations can be represented with Gaussian statistics, leading to proper view- and light-dependent filtering results. Our results match ground truth and our solution is well suited to real-time applications, requires only a few- lines of shader code (provided in supplemental material, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TVCG.2013.102), is high performance, and has a negligible memory footprint.
Keywords :
Gaussian processes; image colour analysis; image enhancement; image representation; image resolution; image texture; nonlinear filters; nonlinear functions; rendering (computer graphics); table lookup; transfer functions; Gaussian statistics; aliasing reduction; color map distribution representation; geometric detail; geometry-dependent functions; high-resolution color mapped microsurface details; irradiance environment maps; light-dependent filtering; look-up tables; nonlinear transfer function filtering; occlusion effects; physical-based rendering quantity; pixel footprint; procedural noise; rendering problems; shader code; surface attributes; surface geometry; surface textures; view-dependent filtering; visual detail enhancement; Color; Colored noise; Equations; Geometry; Image color analysis; Transfer functions; Gaussian statistics; LOD; noise; procedural texture;
fLanguage :
English
Journal_Title :
Visualization and Computer Graphics, IEEE Transactions on
Publisher :
ieee
ISSN :
1077-2626
Type :
jour
DOI :
10.1109/TVCG.2013.102
Filename :
6564283
Link To Document :
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