Fourier Opacity Optimization for Scalable Exploration

Over the past decades, scientific visualization became a fundamental aspect of modern scientific data analysis. Across all data-intensive research fields, ranging from structural biology to cosmology, data sizes increase rapidly. Dealing with the growing large-scale data is one of the top research c...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics 2020-11, Vol.26 (11), p.3204-3216
Main Authors: Rojo, Irene Baeza, Gross, Markus, Gunther, Tobias
Format: Article
Language:English
Subjects:
Astrophysics
Cameras
Cosmology
Data analysis
Data visualization
Fourier approximation
Geometry
Meteorology
Navigation
Occlusion
Opacity
opacity optimization
Optical thickness
Optimization
Organic chemistry
Rendering (computer graphics)
Scientific visualization
Visibility
Visualization
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Summary:Over the past decades, scientific visualization became a fundamental aspect of modern scientific data analysis. Across all data-intensive research fields, ranging from structural biology to cosmology, data sizes increase rapidly. Dealing with the growing large-scale data is one of the top research challenges of this century. For the visual exploratory data analysis, interactivity, a view-dependent visibility optimization and frame coherence are indispensable. In this work, we extend the recent decoupled opacity optimization framework to enable a navigation without occlusion of important features through large geometric data. By expressing the accumulation of importance and optical depth in Fourier basis, the computation, evaluation and rendering of optimized transparent geometry become not only order-independent, but also operate within a fixed memory bound. We study the quality of our Fourier approximation in terms of accuracy, memory requirements and efficiency for both the opacity computation, as well as the order-independent compositing. We apply the method to different point, line and surface data sets originating from various research fields, including meteorology, health science, astrophysics and organic chemistry.
ISSN:1077-2626
1941-0506
DOI:10.1109/TVCG.2019.2915222