2017
Samsel, Francesca; Patchett, John; Rogers, David; Tsai, Karen
Employing Color Theory to Visualize Volume-rendered Multivariate Ensembles of Asteroid Impact Simulations Proceedings Article
In: Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems, pp. 1126-1134, ACM, 2017, ISBN: 978-1-4503-4656-6, (LA-UR-17-20419).
Abstract | Links | BibTeX | Tags: colormaps, ensemble visualization, scientific visualization, visualization design, volume rendering
@inproceedings{LAPR-2017-027464,
title = {Employing Color Theory to Visualize Volume-rendered Multivariate Ensembles of Asteroid Impact Simulations},
author = {Francesca Samsel and John Patchett and David Rogers and Karen Tsai},
url = {http://doi.acm.org/10.1145/3027063.3053337},
doi = {10.1145/3027063.3053337},
isbn = {978-1-4503-4656-6},
year = {2017},
date = {2017-05-06},
booktitle = {Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems},
pages = {1126-1134},
publisher = {ACM},
series = {CHI EA '17},
abstract = {We describe explorations and innovations developed to help scientists understand an ensemble of large scale simulations of asteroid impacts in the ocean. The simulations were run to help scientists determine the characteristics of asteroids that NASA should track, so that communities at risk from impact can be given advanced notice. Of relevance to the CHI community are 1) hands-on workflow issues specific to exploring ensembles of large scientific data, 2) innovations in exploring such data ensembles with color, and 3) examples of multidisciplinary collaboration.},
note = {LA-UR-17-20419},
keywords = {colormaps, ensemble visualization, scientific visualization, visualization design, volume rendering},
pubstate = {published},
tppubtype = {inproceedings}
}
We describe explorations and innovations developed to help scientists understand an ensemble of large scale simulations of asteroid impacts in the ocean. The simulations were run to help scientists determine the characteristics of asteroids that NASA should track, so that communities at risk from impact can be given advanced notice. Of relevance to the CHI community are 1) hands-on workflow issues specific to exploring ensembles of large scientific data, 2) innovations in exploring such data ensembles with color, and 3) examples of multidisciplinary collaboration.
2003
Woodring, Jonathan; Wang, Chaoli; Shen, Han-Wei
High dimensional direct rendering of time-varying volumetric data Proceedings Article
In: IEEE, 2003.
Abstract | Links | BibTeX | Tags: hyperprojection, hyperslice, inte- gration operator, raycasting, time-varying data, transfer function, volume rendering
@inproceedings{Woodring2003,
title = {High dimensional direct rendering of time-varying volumetric data},
author = {Jonathan Woodring and Chaoli Wang and Han-Wei Shen},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/HighDimensionalDirectRenderingOfTime-VaryingVolumetricData.pdf},
year = {2003},
date = {2003-01-01},
publisher = {IEEE},
abstract = {We present an alternative method for viewing time-varying volu- metric data. We consider such data as a four-dimensional data field, rather than considering space and time as separate entities. If we treat the data in this manner, we can apply high dimensional slicing and projection techniques to generate an image hyperplane. The user is provided with an intuitive user interface to specify arbitrary hyperplanes in 4D, which can be displayed with standard volume rendering techniques. From the volume specification, we are able to extract arbitrary hyperslices, combine slices together into a hy- perprojection volume, or apply a 4D raycasting method to generate the same results. In combination with appropriate integration op- erators and transfer functions, we are able to extract and present different space-time features to the user.},
keywords = {hyperprojection, hyperslice, inte- gration operator, raycasting, time-varying data, transfer function, volume rendering},
pubstate = {published},
tppubtype = {inproceedings}
}
We present an alternative method for viewing time-varying volu- metric data. We consider such data as a four-dimensional data field, rather than considering space and time as separate entities. If we treat the data in this manner, we can apply high dimensional slicing and projection techniques to generate an image hyperplane. The user is provided with an intuitive user interface to specify arbitrary hyperplanes in 4D, which can be displayed with standard volume rendering techniques. From the volume specification, we are able to extract arbitrary hyperslices, combine slices together into a hy- perprojection volume, or apply a 4D raycasting method to generate the same results. In combination with appropriate integration op- erators and transfer functions, we are able to extract and present different space-time features to the user.
2001
Kniss, Joe; McCormick, Patrick; McPherson, Allen; Ahrens, James; Painter, James; Keahey, Alan; Hansen, Charles
TRex: Interactive Texture Based Volume Rendering for Extremely Large Datasets Journal Article
In: 2001, (LA-UR-01-1723).
Abstract | Links | BibTeX | Tags: interactive, texture, volume rendering
@article{knissytrex,
title = {TRex: Interactive Texture Based Volume Rendering for Extremely Large Datasets},
author = {Joe Kniss and Patrick McCormick and Allen McPherson and James Ahrens and James Painter and Alan Keahey and Charles Hansen},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/TRexInteractiveTextureBasedVolumeRenderingForExtremelyLargeDatasets.pdf},
year = {2001},
date = {2001-08-01},
abstract = {Many of today’s scientific simulations are capable of producing terabytes to petabytes of data. Visualization plays a critical role in understanding and analyzing the results of these simulations. Hardware accelerated direct volume rendering has proven to be an excellent visualization modality for both scientific and medical data sets. Current graphics hardware implementations limit the size of interactive datasets to sizes that are orders of magnitude smaller than these datasets. We present a scalable system which takes advantage of parallel graphics hardware, software based compositing, and high performance I/O. The goals of our application are to provide near interactive display rates for terabyte sized, time-varying, datasets and allow moderately sized datasets to be visualized in virtual environments. We also present a novel set of direct manipulation widgets for interacting with, and querying, the visualization.},
note = {LA-UR-01-1723},
keywords = {interactive, texture, volume rendering},
pubstate = {published},
tppubtype = {article}
}
Many of today’s scientific simulations are capable of producing terabytes to petabytes of data. Visualization plays a critical role in understanding and analyzing the results of these simulations. Hardware accelerated direct volume rendering has proven to be an excellent visualization modality for both scientific and medical data sets. Current graphics hardware implementations limit the size of interactive datasets to sizes that are orders of magnitude smaller than these datasets. We present a scalable system which takes advantage of parallel graphics hardware, software based compositing, and high performance I/O. The goals of our application are to provide near interactive display rates for terabyte sized, time-varying, datasets and allow moderately sized datasets to be visualized in virtual environments. We also present a novel set of direct manipulation widgets for interacting with, and querying, the visualization.
: . .
1.
Samsel, Francesca; Patchett, John; Rogers, David; Tsai, Karen
Employing Color Theory to Visualize Volume-rendered Multivariate Ensembles of Asteroid Impact Simulations Proceedings Article
In: Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems, pp. 1126-1134, ACM, 2017, ISBN: 978-1-4503-4656-6, (LA-UR-17-20419).
@inproceedings{LAPR-2017-027464,
title = {Employing Color Theory to Visualize Volume-rendered Multivariate Ensembles of Asteroid Impact Simulations},
author = {Francesca Samsel and John Patchett and David Rogers and Karen Tsai},
url = {http://doi.acm.org/10.1145/3027063.3053337},
doi = {10.1145/3027063.3053337},
isbn = {978-1-4503-4656-6},
year = {2017},
date = {2017-05-06},
booktitle = {Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems},
pages = {1126-1134},
publisher = {ACM},
series = {CHI EA '17},
abstract = {We describe explorations and innovations developed to help scientists understand an ensemble of large scale simulations of asteroid impacts in the ocean. The simulations were run to help scientists determine the characteristics of asteroids that NASA should track, so that communities at risk from impact can be given advanced notice. Of relevance to the CHI community are 1) hands-on workflow issues specific to exploring ensembles of large scientific data, 2) innovations in exploring such data ensembles with color, and 3) examples of multidisciplinary collaboration.},
note = {LA-UR-17-20419},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We describe explorations and innovations developed to help scientists understand an ensemble of large scale simulations of asteroid impacts in the ocean. The simulations were run to help scientists determine the characteristics of asteroids that NASA should track, so that communities at risk from impact can be given advanced notice. Of relevance to the CHI community are 1) hands-on workflow issues specific to exploring ensembles of large scientific data, 2) innovations in exploring such data ensembles with color, and 3) examples of multidisciplinary collaboration.
2.
Woodring, Jonathan; Wang, Chaoli; Shen, Han-Wei
High dimensional direct rendering of time-varying volumetric data Proceedings Article
In: IEEE, 2003.
@inproceedings{Woodring2003,
title = {High dimensional direct rendering of time-varying volumetric data},
author = {Jonathan Woodring and Chaoli Wang and Han-Wei Shen},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/HighDimensionalDirectRenderingOfTime-VaryingVolumetricData.pdf},
year = {2003},
date = {2003-01-01},
publisher = {IEEE},
abstract = {We present an alternative method for viewing time-varying volu- metric data. We consider such data as a four-dimensional data field, rather than considering space and time as separate entities. If we treat the data in this manner, we can apply high dimensional slicing and projection techniques to generate an image hyperplane. The user is provided with an intuitive user interface to specify arbitrary hyperplanes in 4D, which can be displayed with standard volume rendering techniques. From the volume specification, we are able to extract arbitrary hyperslices, combine slices together into a hy- perprojection volume, or apply a 4D raycasting method to generate the same results. In combination with appropriate integration op- erators and transfer functions, we are able to extract and present different space-time features to the user.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We present an alternative method for viewing time-varying volu- metric data. We consider such data as a four-dimensional data field, rather than considering space and time as separate entities. If we treat the data in this manner, we can apply high dimensional slicing and projection techniques to generate an image hyperplane. The user is provided with an intuitive user interface to specify arbitrary hyperplanes in 4D, which can be displayed with standard volume rendering techniques. From the volume specification, we are able to extract arbitrary hyperslices, combine slices together into a hy- perprojection volume, or apply a 4D raycasting method to generate the same results. In combination with appropriate integration op- erators and transfer functions, we are able to extract and present different space-time features to the user.
3.
Kniss, Joe; McCormick, Patrick; McPherson, Allen; Ahrens, James; Painter, James; Keahey, Alan; Hansen, Charles
TRex: Interactive Texture Based Volume Rendering for Extremely Large Datasets Journal Article
In: 2001, (LA-UR-01-1723).
@article{knissytrex,
title = {TRex: Interactive Texture Based Volume Rendering for Extremely Large Datasets},
author = {Joe Kniss and Patrick McCormick and Allen McPherson and James Ahrens and James Painter and Alan Keahey and Charles Hansen},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/TRexInteractiveTextureBasedVolumeRenderingForExtremelyLargeDatasets.pdf},
year = {2001},
date = {2001-08-01},
abstract = {Many of today’s scientific simulations are capable of producing terabytes to petabytes of data. Visualization plays a critical role in understanding and analyzing the results of these simulations. Hardware accelerated direct volume rendering has proven to be an excellent visualization modality for both scientific and medical data sets. Current graphics hardware implementations limit the size of interactive datasets to sizes that are orders of magnitude smaller than these datasets. We present a scalable system which takes advantage of parallel graphics hardware, software based compositing, and high performance I/O. The goals of our application are to provide near interactive display rates for terabyte sized, time-varying, datasets and allow moderately sized datasets to be visualized in virtual environments. We also present a novel set of direct manipulation widgets for interacting with, and querying, the visualization.},
note = {LA-UR-01-1723},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Many of today’s scientific simulations are capable of producing terabytes to petabytes of data. Visualization plays a critical role in understanding and analyzing the results of these simulations. Hardware accelerated direct volume rendering has proven to be an excellent visualization modality for both scientific and medical data sets. Current graphics hardware implementations limit the size of interactive datasets to sizes that are orders of magnitude smaller than these datasets. We present a scalable system which takes advantage of parallel graphics hardware, software based compositing, and high performance I/O. The goals of our application are to provide near interactive display rates for terabyte sized, time-varying, datasets and allow moderately sized datasets to be visualized in virtual environments. We also present a novel set of direct manipulation widgets for interacting with, and querying, the visualization.