A single-pass GPU ray casting framework for interactive out-of-core rendering of massive volumetric datasets
Enrico Gobbetti, Fabio Marton, and José Antonio Iglesias Guitián
2008
Abstract
We present an adaptive out-of-core technique for rendering massive scalar volumes employing single pass GPU raycasting. The method is based on the decomposition of a volumetric dataset into small cubical bricks, which are then organized into an octree structure maintained out-of-core. The octree contains the original data at the leaves, and a filtered representation of children at inner nodes. At runtime an adaptive loader, executing on the CPU, updates a view- and transfer function-dependent working set of bricks maintained on GPU memory by asynchronously fetching data from the out-of-core octree representation. At each frame, a compact indexing structure, which spatially organizes the current working set into an octree hierarchy, is encoded in a small texture. This data structure is then exploited by an efficient stackless raycasting algorithm, which computes the volume rendering integral by visiting non-empty bricks in front-to-back order and adapting sampling density to brick resolution. Block visibility information is fed back to the loader to avoid refinement and data loading of occluded zones. The resulting method is able to interactively explore multi-giga-voxel datasets on a desktop PC.
Reference and download information
Enrico Gobbetti, Fabio Marton, and José Antonio Iglesias Guitián. A single-pass GPU ray casting framework for interactive out-of-core rendering of massive volumetric datasets. The Visual Computer, 24(7-9): 797-806, 2008. Proc. CGI 2008.
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Bibtex citation record
@Article{Gobbetti:2008:SGR, author = {Enrico Gobbetti and Fabio Marton and {Jos\'e Antonio} {Iglesias Guiti\'an}}, title = {A single-pass {GPU} ray casting framework for interactive out-of-core rendering of massive volumetric datasets}, journal = {The Visual Computer}, volume = {24}, number = {7-9}, pages = {797--806}, year = {2008}, abstract = { We present an adaptive out-of-core technique for rendering massive scalar volumes employing single pass GPU raycasting. The method is based on the decomposition of a volumetric dataset into small cubical bricks, which are then organized into an octree structure maintained out-of-core. The octree contains the original data at the leaves, and a filtered representation of children at inner nodes. At runtime an adaptive loader, executing on the CPU, updates a view- and transfer function-dependent working set of bricks maintained on GPU memory by asynchronously fetching data from the out-of-core octree representation. At each frame, a compact indexing structure, which spatially organizes the current working set into an octree hierarchy, is encoded in a small texture. This data structure is then exploited by an efficient stackless raycasting algorithm, which computes the volume rendering integral by visiting non-empty bricks in front-to-back order and adapting sampling density to brick resolution. Block visibility information is fed back to the loader to avoid refinement and data loading of occluded zones. The resulting method is able to interactively explore multi-giga-voxel datasets on a desktop PC. }, note = {Proc. CGI 2008}, url = {http://vic.crs4.it/vic/cgi-bin/bib-page.cgi?id='Gobbetti:2008:SGR'}, }
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