Hybrid Data Visualization Based on Depth Complexity Histogram Analysis

In many cases, only the combination of geometric and volumetric data sets is able to describe a single phenomenon under observation when visualizing large and complex data. When semi‐transparent geometry is present, correct rendering results require sorting of transparent structures. Additional comp...

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Bibliographic Details
Published in:Computer graphics forum Vol. 34; no. 1; pp. 74 - 85
Main Authors: Lindholm, S., Falk, M., Sundén, E., Bock, A., Ynnerman, A., Ropinski, T.
Format: Journal Article
Language:English
Published: Oxford Blackwell Publishing Ltd 01.02.2015
Subjects:
Algorithms
Analysis
Buffers
Complexity
Computer graphics
Computer memory
Data processing
Data visualization
Gain
Histograms
I.3.3 [Computer Graphics]: Picture/Image Generation Display-algorithms
I.3.3 [Computer Graphics]: Picture/Image Generation Display—algorithms; I.3.7 [Computer Graphics]: Three‐Dimensional Graphics and Realism—I.3.8 [Computer Graphics]: Applications
I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism-I.3.8 [Computer Graphics]: Applications
Molecular biology
Pixels
ray tracing
real-time rendering
Rendering
Scientific visualization
Space weather
Studies
Utilization
Visualization
ISSN:0167-7055, 1467-8659, 1467-8659
Online Access:Get full text
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Summary:In many cases, only the combination of geometric and volumetric data sets is able to describe a single phenomenon under observation when visualizing large and complex data. When semi‐transparent geometry is present, correct rendering results require sorting of transparent structures. Additional complexity is introduced as the contributions from volumetric data have to be partitioned according to the geometric objects in the scene. The A‐buffer, an enhanced framebuffer with additional per‐pixel information, has previously been introduced to deal with the complexity caused by transparent objects. In this paper, we present an optimized rendering algorithm for hybrid volume‐geometry data based on the A‐buffer concept. We propose two novel components for modern GPUs that tailor memory utilization to the depth complexity of individual pixels. The proposed components are compatible with modern A‐buffer implementations and yield performance gains of up to eight times compared to existing approaches through reduced allocation and reuse of fast cache memory. We demonstrate the applicability of our approach and its performance with several examples from molecular biology, space weather and medical visualization containing both, volumetric data and geometric structures. We present an A‐buffer based algorithm that achieves performance gains of up to eight times relative existing techniques. The algorithm contains two novel components which improve the utilization of the local cache memory on the GPU. This is particularly important for scenes with non‐uniform depth complexities and rapidly decreasing depth complexity histograms (DCHs).
Bibliography:istex:4AA5C4DDE5B9DA11B07EDDAAD8777A0DF99B0AD1
Community Coordinated Modeling Center at Goddard Space Flight Center, NASA
Figure S1: Performance comparison with our proposed ppAO and ppDP components.Movie S1: Simultaneous visualization of both volumetric and geometric objects relies heavily on techniques such as A-buffers to achieve real-time performance. This work proposes two novel A-buffer components which achieve performance gains of up to eight times relative existing techniques.
ark:/67375/WNG-BL6BLS0D-4
Swedish Research Council - No. 2011-5816
Linnaeus Environment CADICS and the Swedish e-Science Research Centre (SeRC)
ArticleID:CGF12460
SourceType-Scholarly Journals-1
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ISSN:0167-7055
1467-8659
1467-8659
DOI:10.1111/cgf.12460