Spatio-Temporal Contours from Deep Volume Raycasting
S. Frey. Computer Graphics Forum, 37 (3):
513-524(2018)20th Eurographics/IEEE VGTC Conference on Visualization (EuroVis), Brno, Czech Republic, Jun 04-08, 2018.
DOI: 10.1111/cgf.13438
Abstract
We visualize contours for spatio‐temporal processes to indicate where and when non‐continuous changes occur or spatial bounds are encountered. All time steps are comprised densely in one visualization, with contours allowing to efficiently analyze processes in the data even in case of spatial or temporal overlap. Contours are determined on the basis of deep raycasting that collects samples across time and depth along each ray. For each sample along a ray, its closest neighbors from adjacent rays are identified, considering time, depth, and value in the process. Large distances are represented as contours in image space, using color to indicate temporal occurrence. This contour representation can easily be combined with volume rendering‐based techniques, providing both full spatial detail for individual time steps and an outline of the whole time series in one view. Our view‐dependent technique supports efficient progressive computation, and requires no prior assumptions regarding the shape or nature of processes in the data. We discuss and demonstrate the performance and utility of our approach via a variety of data sets, comparison and combination with an alternative technique, and feedback by a domain scientist.
Description
Spatio-Temporal Contours from Deep Volume Raycasting
IEEE Visualizat & Graph Tech Comm; Univ Masarykiana, Fac Artis Informatice; Univ Masarykiana Brunensis; European Assoc Com Graph; Nvidia; Intel; King Abdullah Univ Sci & Technol; Kiwi Com; A V Media
journal
Computer Graphics Forum
number
3
pages
513-524
publisher
Wiley
volume
37
type
Article; Proceedings Paper
research-areas
Computer Science
eissn
1467-8659
language
English
issn
0167-7055
affiliation
Frey, S (Reprint Author), Univ Stuttgart, Visualizat Res Ctr, Stuttgart, Germany. Frey, S., Univ Stuttgart, Visualizat Res Ctr, Stuttgart, Germany.
%0 Journal Article
%1 frey2018spatiotemporal
%A Frey, Steffen
%D 2018
%I Wiley
%J Computer Graphics Forum
%K from:leonkokkoliadis A02 2018 sfbtrr161 from:mueller visus visus:freysn
%N 3
%P 513-524
%R 10.1111/cgf.13438
%T Spatio-Temporal Contours from Deep Volume Raycasting
%U https://doi.org/10.1111/cgf.13438
%V 37
%X We visualize contours for spatio‐temporal processes to indicate where and when non‐continuous changes occur or spatial bounds are encountered. All time steps are comprised densely in one visualization, with contours allowing to efficiently analyze processes in the data even in case of spatial or temporal overlap. Contours are determined on the basis of deep raycasting that collects samples across time and depth along each ray. For each sample along a ray, its closest neighbors from adjacent rays are identified, considering time, depth, and value in the process. Large distances are represented as contours in image space, using color to indicate temporal occurrence. This contour representation can easily be combined with volume rendering‐based techniques, providing both full spatial detail for individual time steps and an outline of the whole time series in one view. Our view‐dependent technique supports efficient progressive computation, and requires no prior assumptions regarding the shape or nature of processes in the data. We discuss and demonstrate the performance and utility of our approach via a variety of data sets, comparison and combination with an alternative technique, and feedback by a domain scientist.
@article{frey2018spatiotemporal,
abstract = {We visualize contours for spatio‐temporal processes to indicate where and when non‐continuous changes occur or spatial bounds are encountered. All time steps are comprised densely in one visualization, with contours allowing to efficiently analyze processes in the data even in case of spatial or temporal overlap. Contours are determined on the basis of deep raycasting that collects samples across time and depth along each ray. For each sample along a ray, its closest neighbors from adjacent rays are identified, considering time, depth, and value in the process. Large distances are represented as contours in image space, using color to indicate temporal occurrence. This contour representation can easily be combined with volume rendering‐based techniques, providing both full spatial detail for individual time steps and an outline of the whole time series in one view. Our view‐dependent technique supports efficient progressive computation, and requires no prior assumptions regarding the shape or nature of processes in the data. We discuss and demonstrate the performance and utility of our approach via a variety of data sets, comparison and combination with an alternative technique, and feedback by a domain scientist.},
added-at = {2020-10-09T12:31:49.000+0200},
affiliation = {Frey, S (Reprint Author), Univ Stuttgart, Visualizat Res Ctr, Stuttgart, Germany. Frey, S., Univ Stuttgart, Visualizat Res Ctr, Stuttgart, Germany.},
author = {Frey, Steffen},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2a8b4a6f53cce8ab52f6399a168d2e965/visus},
da = {2019-03-28},
description = {Spatio-Temporal Contours from Deep Volume Raycasting},
doi = {10.1111/cgf.13438},
eissn = {1467-8659},
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intrahash = {a8b4a6f53cce8ab52f6399a168d2e965},
issn = {0167-7055},
journal = {Computer Graphics Forum},
keywords = {from:leonkokkoliadis A02 2018 sfbtrr161 from:mueller visus visus:freysn},
language = {English},
note = {20th Eurographics/IEEE VGTC Conference on Visualization (EuroVis), Brno, Czech Republic, Jun 04-08, 2018},
number = 3,
organization = {IEEE Visualizat \& Graph Tech Comm; Univ Masarykiana, Fac Artis Informatice; Univ Masarykiana Brunensis; European Assoc Com Graph; Nvidia; Intel; King Abdullah Univ Sci \& Technol; Kiwi Com; A V Media},
pages = {513-524},
publisher = {Wiley},
research-areas = {Computer Science},
timestamp = {2020-10-09T10:31:49.000+0200},
title = {Spatio-Temporal Contours from Deep Volume Raycasting},
type = {Article; Proceedings Paper},
unique-id = {ISI:000438024300045},
url = {https://doi.org/10.1111/cgf.13438},
volume = 37,
year = 2018
}