Real-time display and manipulation of 3-D medical objects: the Voxel processor architecture
Computer Vision, Graphics, and Image Processing
Hierarchical Data Structures and Algorithms for Computer Graphics. Part I.
IEEE Computer Graphics and Applications
Adjacency algorithms for linear octree nodes
Image and Vision Computing
Representation of Three-Dimensional Digital Images
ACM Computing Surveys (CSUR)
The Quadtree and Related Hierarchical Data Structures
ACM Computing Surveys (CSUR)
Hierarchical Data Structures and Algorithms for Computer Graphics
IEEE Computer Graphics and Applications
Algorithms for the physical rendering and assembly of octree models
Computer-Aided Design
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The storage, display, and manipulation of three dimensional volumetric information requires large amounts of computing resources, both in terms of memory, and processing power. Most existing serial algorithms that display 3-D objects on a 2-D screen are found to be too slow to process the large amounts of volume data in a reasonable time. Hence, one way to increase the performance of the display algorithm is to process individual volume elements (voxels) in parallel. The first part of this paper presents a brief over view of the linear octree data structure which represents 3-D objects by an eight-way branching tree, while the second part focusses on the parallel display of such objects. We have shown that, for an object represented by a linear octree and enclosed in a 2n×2n×2n universe, the maximum number of voxels that can be processed in parallel is 3n, and the maximum number of time steps required to display such an object is 4n. This paper presents a set of formulae which identify the processing element (PE) as well as the time step in which a given linear octree node is processed. Similarly, a procedure which determines the locational code of a linear octree node which must be processed by a given PE, at some specific time step, is presented, along with a strategy for determining whether a PE is active or idle