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S Grimm, St. Bruckner, A. Kanitsar, E. Gröller:
"Memory Efficient Acceleration Structures and Techniques for CPU-based Volume Raycasting of Large Data";
Talk: IEEE/SIGGRAPH Symp. on Volume Visualization and Graphics 2004, Austin, Texas; 2004-10-11 - 2004-10-12; in: "Symposium on Volume Visualization", D Silver, T. Ertl, C Silva (ed.); IEEE, (2004), ISBN: 0780387813; 1 - 8.

Most CPU-based volume raycasting approaches achieve high performance by advanced memory layouts, space subdivision, and excessive pre-computing. Such approaches typically need an enormous amount of memory. They are limited to sizes which do not satisfy the medical data used in daily clinical routine. We present a new volume raycasting approach based on image-ordered raycasting with object-ordered processing, which is able to perform high-quality rendering of very large medical data in real-time on commodity computers. For large medical data such as computed tomographic (CT) angiography run-offs (512x512x1202) we achieve rendering times up to 2.5 fps on a commodity notebook. We achieve this by introducing a memory efficient acceleration technique for on-the-fly gradient estimation and a memory efficient hybrid removal and skipping technique of transparent regions. We employ quantized binary histograms, granular resolution octrees, and a cell invisibility cache. These acceleration structures require just a small extra storage of approximately 10%.

http://aleph.ub.tuwien.ac.at/F?base=tuw01&func=find-c&ccl_term=AC04968546

http://www.cg.tuwien.ac.at/research/publications/2004/grimm-2004-memory/

Project Head Eduard Gröller:
FFF Adapt = Abt. 186/2