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One billion polygons to billions of pixels |
Welcome to the first gigapixel, multi-view rendering of The Digital Michelangelo Project's David.
The David model consists of 933 million triangles from a laser-scan of the original statue created by Professor Marc Levoy and members of The Digital Michelangelo Project at Stanford university. The model was aligned by Benedict Brown and Szymon Rusinkiewicz using the non-rigid alignment method described in their 2007 SIGGRAPH paper.
Each of the 4 2-gigapixel sized frames (29280 x 70416 pixels) was rendered using the Manta Interactive Ray Tracer. Manta is a highly portable interactive ray tracing environment designed at the SCI Institute to be used on both workstations and super computers. For these renderings, Manta leveraged a recursive 4-level grid to accelerate the rendering. In all, each frame took 30 hours to render using 64 cores each (256 total) of the SCI Institute's 264 core SGI UV 1000 with 2.8TB of RAM and 2.67GHz Intel Xeon X7542 cores. More information on Manta can be found at:
http://mantawiki.sci.utah.edu/manta/index.php/Main_Page
The final rendering was stored in the hierarchical, space-filling curve format of the ViSUS technology. ViSUS intelligently reorganizes the raw data enabling efficient, streaming pipelines that process the information while in movement. The results are then visualized in a progressive environment allowing for meaningful explorations with minimal required resources. This technology enables real-time management of large datasets on a variety of systems ranging from desktops and laptop computers to portable devices such as iPhones/iPads. ViSUS has been deployed in a variety of large data applications such as the monitoring of large scientific simulations and the editing of massive images and panoramas.
The ViSUS David viewer is currently available as a Windows web browser plugin (Firefox and Chrome) or as a standalone application for Windows, Max OS X, or OpenSUSE. Please follow the links below to access the gigapixel David.
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Uncertainty Visualization |
The graphical depiction of uncertainty information is emerging as a problem of great importance in the field of visualization. Scientific data sets are not considered complete without indications of error, accuracy, or levels of confidence, and this information is often presented as charts and tables alongside visual representations of the data. Uncertainty measures are often excluded from explicit representation within data visualizations because the increased visual complexity incurred can cause clutter, obscure the data display, and may lead to erroneous conclusions or false predictions. However, uncertainty is an essential component of the data, and its display must be integrated in order for a visualization to be considered a true representation of the data. The growing need for the addition of qualitative information into the visual representation of data, and the challenges associated with that need, command fundamental research on the visualization of uncertainty.
Projects
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ProbVis: Interactive visualization of probability distribution functions.
The ProbVis software tool allows for the interactive display and exploration of a spatial collection of data distributions. A global display shows the value of a difference measure across the spatial domain. The user can change the measure from the L1 Norm to the Hellinger distance. The user is also given a pointer to explore the individual distributions
which are diplayed as a PDF or CDF in the lower corner. |
Interactive Visualization of Probability and Cumulative Density Functions
Kristin Potter, Robert M. Kirby, Dongbin Xiu, & Chris R. Johnson
International Journal for Uncertainty Quantification, to appear. 2011. |
Meeting Schedule
Meetings are held every other Tuesday at 2pm in the Jones conference room unless otherwise noted.
Spring 2011
| Date |
Speaker |
Topic |
Note |
| 1/11 |
Jeff Phillips |
There is uncertainty in your uncertainty |
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| 1/25 |
Fangxiang Jiao |
Review of uncertainty analysis and uncertainty visualization in Diffusion Tensor Imaging (DTI) |
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| 2/08 |
Liang Zhou |
Transfer function combinations |
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| 2/22 |
Shreeraj Jadhav |
Uncertain 2D Vector Field Topology |
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| 3/08 |
Mike Kirby |
Overview of the stochastic Galerkin and stochastic collocation methods |
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| 4/05 |
Kristi Potter |
A prototype for Material Models |
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| 4/19 |
Tobias Martin, Guoning Chen, and Suraj Musuvathy |
Extraction and Harmonic Parameterization of Topology-Consistent Midstructures |
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| 5/03 |
Josh Levine |
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People
Links
Uncertainty Visualization Reference Library
Publications
H. Bhatia, S. Jadhav, P.-T. Bremer, G. Chen, J.A. Levine, L.G. Nonato, V. Pascucci. “Edge Maps: Representing Flow with Bounded Error,” InProceedings of IEEE Pacific Visualization Symposium 2011, Hong Kong, China, pp. (accepted). March, 2011.
K. Potter, J.M. Kniss, R. Riesenfeld, C.R. Johnson. “Visualizing Summary Statistics and Uncertainty,” In Computer Graphics Forum (Proceedings of Eurovis 2010), Vol. 29, No. 3, pp. 823--831. 2010.
K. Potter, A. Wilson, P.-T. Bremer, D. Williams, C. Doutriaux, V. Pascucci, C.R. Johhson. “Visualization of Uncertainty and Ensemble Data: Exploration of Climate Modeling and Weather Forecast Data with Integrated ViSUS-CDAT Systems,” In Proceedings of SciDAC 2009, Journal of Physics: Conference Series, Vol. 180, No. 012089, pp. (published online). 2009.
K. Potter, A. Wilson, P.-T. Bremer, D. Williams, C. Doutriaux, V. Pascucci, C.R. Johnson. “Ensemble-Vis: A Framework for the Statistical Visualization of Ensemble Data,” In Proceedings of the 2009 IEEE International Conference on Data Mining Workshops, pp. 233--240. 2009.
K. Potter, J. Krueger, C.R. Johnson. “Towards the Visualization of Multi-Dimentional Stochastic Distribution Data,” In Proceedings of The International Conference on Computer Graphics and Visualization (IADIS) 2008, pp. 191--196. 2008.
J.M. Kniss, R. Van Uitert, A.J. Stephens, G. Li, T. Tasdizen. “Statistically Quantitative Volume Visualization,” In IEEE Visualization 2005, 2005.
C.R. Johnson. “Top Scientific Visualization Research Problems,” In IEEE Computer Graphics and Applications: Visualization Viewpoints, Vol. 24, No. 4, pp. 13--17. July/August, 2004.
G. Kindlmann, R.T. Whitaker, T. Tasdizen, T. Möller. “Curvature-Based Transfer Functions for Direct Volume Rendering: Methods and Applications,” In Proceedings Visualization 2003, pp. 67. October, 2003.
C.R. Johnson, A.R. Sanderson. “A Next Step: Visualizing Errors and Uncertainty,” In IEEE Computer Graphics and Applications, Vol. 23, No. 5, Edited by Theresa-Marie Rhyne, pp. 6--10. September/October, 2003.
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Analysis and Visualization of Stochastic Simulation Solutions
Acknowledgement:
This material is based upon collaborative work supported by the National Science Foundation under Grant No.IIS-0914564 (Kirby) and IIS-0914447 (Xiu).
Disclaimer:
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Award title:
AF: Small: Collaborative Research: Analysis and Visualization of Stochastic Simulation Solutions
Duration: September 15, 2009 through August 31, 2012
Research Challenges
In this age of scientific computing, the simulation science pipeline of mathematical modeling, simulation and evaluation is a commonly employed rendition of the scientific method. In addition to the traditional components of the pipeline, there has been a recent surge of interest in uncertainty quantification (UQ). Visualization is the window through which scientists examine their data for deriving new science, and hence visualization methods which depict underlying uncertainty information are crucial. This research addresses the questions of how does one accurately and efficiently post-process stochastic simulation fields and how does one effectively and succinctly convey the results. This is accomplished by developing strategies and techniques for augmenting current visualization techniques used for visualizing spatio-temporal fields with UQ information in a seamless way.
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