Local ambient occlusion in direct volume rendering

This paper presents a novel technique to efficiently compute illumination for Direct Volume Rendering using a local approximation of ambient occlusion to integrate the intensity of incident light for each voxel. An advantage with this local approach is that fully shadowed regions are avoided, a desi...

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Veröffentlicht in:IEEE transactions on visualization and computer graphics. - 1996. - 16(2010), 4 vom: 23. Juli, Seite 548-59
1. Verfasser: Hernell, Frida (VerfasserIn)
Weitere Verfasser: Ljung, Patric, Ynnerman, Anders
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2010
Zugriff auf das übergeordnete Werk:IEEE transactions on visualization and computer graphics
Schlagworte:Journal Article Research Support, Non-U.S. Gov't
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520 |a This paper presents a novel technique to efficiently compute illumination for Direct Volume Rendering using a local approximation of ambient occlusion to integrate the intensity of incident light for each voxel. An advantage with this local approach is that fully shadowed regions are avoided, a desirable feature in many applications of volume rendering such as medical visualization. Additional transfer function interactions are also presented, for instance, to highlight specific structures with luminous tissue effects and create an improved context for semitransparent tissues with a separate absorption control for the illumination settings. Multiresolution volume management and GPU-based computation are used to accelerate the calculations and support large data sets. The scheme yields interactive frame rates with an adaptive sampling approach for incrementally refined illumination under arbitrary transfer function changes. The illumination effects can give a better understanding of the shape and density of tissues and so has the potential to increase the diagnostic value of medical volume rendering. Since the proposed method is gradient-free, it is especially beneficial at the borders of clip planes, where gradients are undefined, and for noisy data sets 
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700 1 |a Ynnerman, Anders  |e verfasserin  |4 aut 
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