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

Sören Grimm, Stefan Bruckner, Armin Kanitsar, M. Eduard Gröller

CONFERENCE PAPER: In Proceedings of IEEE VolVis 2004, pp. 1–8, 2004. DOI: 10.1109/SVVG.2004.8

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%.

Published

Proceedings of IEEE VolVis 2004

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BibTeX

@INPROCEEDINGS{Grimm-2004-MEA,
  author = {S{\"o}ren Grimm and Stefan Bruckner and Armin Kanitsar and M.
	Eduard Gr{\"o}ller},
  title = {Memory Efficient Acceleration Structures and Techniques for {CPU}-based
	Volume Raycasting of Large Data},
  booktitle = {Proceedings of IEEE VolVis 2004},
  year = {2004},
  editor = {D. Silver, T. Ertl, C. Silva},
  pages = {1--8},
  month = oct,
  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%.},
  doi = {10.1109/SVVG.2004.8},
  isbn = {0-7803-8781-3},
  keywords = {volume rendering, acceleration, large data},
  url = {http://www.cg.tuwien.ac.at/research/publications/2004/grimm-2004-memory/}
}






 Last Modified: Stefan Bruckner, 2014-08-15