Title
Adaptive sampling in three dimensions for volume rendering on GPUs
Abstract
Direct volume rendering of large volumetric data sets on program- mable graphics hardware is often limited by the amount of available graphics memory and the bandwidth from main memory to graph- ics memory. Therefore, several approaches to volume rendering from compact representations of volumetric data have been pub- lished that avoid most of the data transfer between main memory and the graphics programming unit (GPU) at the cost of additional data decompression by the GPU. To reduce this performance cost, adaptive sampling techniques were proposed; which are, however, usually restricted to the sampling in view direction. In this work, we present a GPU-based volume rendering algo- rithm with adaptive sampling in all three spatial directions; i.e., not only in view direction but also in the two perpendicular directions of the image plane. This approach allows us to reduce the number of samples dramatically without compromising image quality; thus, it is particularly well suited for many compressed representations of volumetric data that require a computational expensive GPU-based sampling of data.
Year
DOI
Venue
2007
10.1109/APVIS.2007.329285
APVIS
Keywords
Field
DocType
computer graphic equipment,data compression,data visualisation,image representation,image resolution,image sampling,rendering (computer graphics),GPU-based volume rendering algorithm,adaptive GPU-based data sampling,data decompression,data transfer,graphics memory,graphics programming unit,image quality,large volumetric data set direct volume rendering,main memory,programmable graphics hardware,scientific visualisation,volume visualization,volumetric data compressed representation
Volume rendering,Computer graphics (images),Computer science,3D rendering,Texture memory,Real-time computer graphics,Software rendering,Rendering (computer graphics),CUDA Pinned memory,Tiled rendering
Conference
Citations 
PageRank 
References 
4
0.48
9
Authors
4
Name
Order
Citations
PageRank
Martin Kraus126915.90
Magnus Strengert222113.43
Thomas Klein340.48
Thomas Ertl44417401.52