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
TR-186-2-03-11, November 2003 [ paper]
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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 the Visible Male (587x341x1878) 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 a small extra storage of approximately 10%.

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@techreport{Grimm-2003-CPU,
  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 =       "2003",
  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 the Visible Male (587x341x1878)
               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 a
               small extra storage of approximately 10%.",
  address =    "Favoritenstrasse 9-11/186, A-1040 Vienna, Austria",
  institution = "Institute of Computer Graphics and Algorithms, Vienna
               University of Technology",
  note =       "human contact: technical-report@cg.tuwien.ac.at",
  month =      nov,
  number =     "TR-186-2-03-11",
  keywords =   "Visible line/surface algorithms, Raytracing,
               Three-Dimensional graphics and realism",
  URL =        "http://www.cg.tuwien.ac.at/research/publications/2003/Grimm-2003-CPU/",
}