Survey of Parallel Volume Rendering Algorithms
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University of Illinois at Chicago tronic Visualization Laboratory (EVL) Survey of Parallel Volume Rendering Algorithms by Craig M. Wittenbrink [email protected] .com Visual Computing Department Hewlett-Packard Laboratories presentation by Nicholas Schwarz [email protected] Electronic Visualization Lab University of Illinois at Chicago
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Survey of Parallel Volume Rendering Algorithms. by Craig M. Wittenbrink [email protected] Visual Computing Department Hewlett-Packard Laboratories. presentation by Nicholas Schwarz [email protected] Electronic Visualization Lab University of Illinois at Chicago. - PowerPoint PPT Presentation
Transcript of Survey of Parallel Volume Rendering Algorithms
University of Illinois at ChicagoElectronic Visualization Laboratory (EVL)
Survey of Parallel Volume Rendering Algorithms
byCraig M. Wittenbrink
[email protected] Computing DepartmentHewlett-Packard Laboratories
presentation byNicholas Schwarz
[email protected] Visualization Lab
University of Illinois at Chicago
University of Illinois at ChicagoElectronic Visualization Laboratory (EVL)
Introduction
• Visualizing large volume data is computationally complex – O(n^3) for an n x n x n volume.
• Use parallelism to achieve interactivity.
• Current classifications make it difficult to determine the best approach.
• Presents a 5 class taxonomy for parallel volume rendering algorithms.
University of Illinois at ChicagoElectronic Visualization Laboratory (EVL)
Volume Rendering Taxonomy
1. Algorithm Control Flow• View Reconstruction
• Backward – Ray Casting• Forward – Splatting• Multipass Forward• Fourier
• Outer Loop Data Space• Object Space• Image Space
University of Illinois at ChicagoElectronic Visualization Laboratory (EVL)
Volume Rendering Taxonomy
2. Targeted Hardware• Graphics (G)• Volume Rendering (VR)• Parallel Shared Address Space (PS)• Parallel Distributed Address Space (PD)• Distributed (D)
University of Illinois at ChicagoElectronic Visualization Laboratory (EVL)
Volume Rendering Taxonomy
3. Application Data Characteristics• Input Topologies
• Rectilinear (R)• Curvilinear (C)• Unstructured (U)
• Data Types• Scalar, Vector, Tensor
• Data Units• Voxel Format
University of Illinois at ChicagoElectronic Visualization Laboratory (EVL)
Volume Rendering Taxonomy
4. Visualization Method• Shading, Data Classification, Transmission
Model, Reconstruction Filter, Gradient
5. Publication Specifics• Date, Number of Processors, Volume Size,
Prototype Machine, Asymptotic Complexity Analysis, and Performance
University of Illinois at ChicagoElectronic Visualization Laboratory (EVL)
Survey
University of Illinois at ChicagoElectronic Visualization Laboratory (EVL)
Links
• http://www.hpl.hp.com/techreports/98/HPL-98-49R1.html
