DocumentCode
402660
Title
Efficient feed-forward volume rendering techniques for vector and parallel processors
Author
Machiraju, Raghu K. ; Yagel, Roni
Author_Institution
Dept. of Comput. & Inf. Sci., Ohio State Univ., OH, USA
fYear
1993
fDate
15-19 Nov. 1993
Firstpage
699
Lastpage
708
Abstract
Rendering volumes represented as a 3D grid of voxels requires an overwhelming amount of processing power. In this paper we investigate efficient techniques for rendering semi-transparent volumes on vector and parallel processors. Parallelism inherent in a regular grid is obtained by decomposing the volume into geometric primitives called beams, slices and slabs of voxels. By using the adjacent properties of voxels in beams and slices, efficient incremental transformation schemes are developed. The slab decomposition of the volume allows the implementation of an efficient parallel feed-forward renderer which includes the splatting technique for image reconstruction and a back-to-front method for creating images. The authors report the implementation of this feed-forward volume renderer on a hierarchical shared memory machine with individual pipelined processors.
Keywords
feedforward; image reconstruction; parallel processing; pipeline processing; rendering (computer graphics); vector processor systems; 3D grid; adjacent properties; back-to-front method; beams; feed-forward volume rendering; geometric primitives; hierarchical shared memory machine; image creation; image reconstruction; incremental transformation schemes; parallel processors; pipelined processors; regular grid; semitransparent volumes; slabs; slices; splatting technique; vector processors; voxels; Concurrent computing; Feedforward systems; Grid computing; Image reconstruction; Information science; Matrix decomposition; Parallel processing; Pixel; Rendering (computer graphics); Slabs;
fLanguage
English
Publisher
ieee
Conference_Titel
Supercomputing '93. Proceedings
ISSN
1063-9535
Print_ISBN
0-8186-4340-4
Type
conf
DOI
10.1109/SUPERC.1993.1263524
Filename
1263524
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