Memory Efficient Acceleration Structures and Techniques for CPU-based Volume Raycasting of Large Data

Sören Grimm, Stefan Bruckner, Armin Kanitsar, Meister Eduard Gröller
Memory Efficient Acceleration Structures and Techniques for CPU-based Volume Raycasting of Large Data
In Proceedings IEEE/SIGGRAPH Symposium on Volume Visualization and Graphics, pages 1-8. October 2004.
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Abstract

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%.

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@inproceedings{grimm-2004-memory,
  title =      "Memory Efficient Acceleration Structures and Techniques for
               CPU-based Volume Raycasting of Large Data",
  author =     "S{\"o}ren Grimm and Stefan Bruckner and Armin Kanitsar and
               Meister Eduard Gr{\"o}ller",
  year =       "2004",
  abstract =   "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%. ",
  pages =      "1--8",
  month =      oct,
  booktitle =  "Proceedings IEEE/SIGGRAPH Symposium on Volume Visualization
               and Graphics",
  editor =     "D. Silver, T. Ertl, C. Silva",
  isbn =       "0-7803-8781-3",
  keywords =   "Three-Dimensional Graphics and Realism,",
  URL =        "http://www.cg.tuwien.ac.at/research/publications/2004/grimm-2004-memory/",
}