Abstract
We introduce Artifact-Based Rendering (ABR), a framework of tools, algorithms, and processes that makes it possible to produce real, data-driven 3D scientific visualizations with a visual language derived entirely from colors, lines, textures, and forms created using traditional physical media or found in nature. A theory and process for ABR is presented to address three current needs: (i) designing better visualizations by making it possible for non-programmers to rapidly design and critique many alternative data-To-visual mappings; (ii) expanding the visual vocabulary used in scientific visualizations to depict increasingly complex multivariate data; (iii) bringing a more engaging, natural, and human-relatable handcrafted aesthetic to data visualization. New tools and algorithms to support ABR include front-end applets for constructing artifact-based colormaps, optimizing 3D scanned meshes for use in data visualization, and synthesizing textures from artifacts. These are complemented by an interactive rendering engine with custom algorithms and interfaces that demonstrate multiple new visual styles for depicting point, line, surface, and volume data. A within-The-research-Team design study provides early evidence of the shift in visualization design processes that ABR is believed to enable when compared to traditional scientific visualization systems. Qualitative user feedback on applications to climate science and brain imaging support the utility of ABR for scientific discovery and public communication.
| Original language | English (US) |
|---|---|
| Article number | 8794607 |
| Pages (from-to) | 492-502 |
| Number of pages | 11 |
| Journal | IEEE Transactions on Visualization and Computer Graphics |
| Volume | 26 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 2020 |
Fingerprint
Keywords
- Art and Visualization
- Data Physicalization
- Multivariate Visualization
- Visualization Design
PubMed: MeSH publication types
- Journal Article
Cite this
Artifact-based rendering : Harnessing natural and traditional visual media for more expressive and engaging 3d visualizations. / Johnson, Seth; Samsel, Francesca; Abram, Gregory; Olson, Daniel; Solis, Andrew J.; Herman, Bridger; Wolfram, Phillip J.; Lenglet, Christophe; Keefe, Daniel F.
In: IEEE Transactions on Visualization and Computer Graphics, Vol. 26, No. 1, 8794607, 01.2020, p. 492-502.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Artifact-based rendering
T2 - Harnessing natural and traditional visual media for more expressive and engaging 3d visualizations
AU - Johnson, Seth
AU - Samsel, Francesca
AU - Abram, Gregory
AU - Olson, Daniel
AU - Solis, Andrew J.
AU - Herman, Bridger
AU - Wolfram, Phillip J.
AU - Lenglet, Christophe
AU - Keefe, Daniel F.
PY - 2020/1
Y1 - 2020/1
N2 - We introduce Artifact-Based Rendering (ABR), a framework of tools, algorithms, and processes that makes it possible to produce real, data-driven 3D scientific visualizations with a visual language derived entirely from colors, lines, textures, and forms created using traditional physical media or found in nature. A theory and process for ABR is presented to address three current needs: (i) designing better visualizations by making it possible for non-programmers to rapidly design and critique many alternative data-To-visual mappings; (ii) expanding the visual vocabulary used in scientific visualizations to depict increasingly complex multivariate data; (iii) bringing a more engaging, natural, and human-relatable handcrafted aesthetic to data visualization. New tools and algorithms to support ABR include front-end applets for constructing artifact-based colormaps, optimizing 3D scanned meshes for use in data visualization, and synthesizing textures from artifacts. These are complemented by an interactive rendering engine with custom algorithms and interfaces that demonstrate multiple new visual styles for depicting point, line, surface, and volume data. A within-The-research-Team design study provides early evidence of the shift in visualization design processes that ABR is believed to enable when compared to traditional scientific visualization systems. Qualitative user feedback on applications to climate science and brain imaging support the utility of ABR for scientific discovery and public communication.
AB - We introduce Artifact-Based Rendering (ABR), a framework of tools, algorithms, and processes that makes it possible to produce real, data-driven 3D scientific visualizations with a visual language derived entirely from colors, lines, textures, and forms created using traditional physical media or found in nature. A theory and process for ABR is presented to address three current needs: (i) designing better visualizations by making it possible for non-programmers to rapidly design and critique many alternative data-To-visual mappings; (ii) expanding the visual vocabulary used in scientific visualizations to depict increasingly complex multivariate data; (iii) bringing a more engaging, natural, and human-relatable handcrafted aesthetic to data visualization. New tools and algorithms to support ABR include front-end applets for constructing artifact-based colormaps, optimizing 3D scanned meshes for use in data visualization, and synthesizing textures from artifacts. These are complemented by an interactive rendering engine with custom algorithms and interfaces that demonstrate multiple new visual styles for depicting point, line, surface, and volume data. A within-The-research-Team design study provides early evidence of the shift in visualization design processes that ABR is believed to enable when compared to traditional scientific visualization systems. Qualitative user feedback on applications to climate science and brain imaging support the utility of ABR for scientific discovery and public communication.
KW - Art and Visualization
KW - Data Physicalization
KW - Multivariate Visualization
KW - Visualization Design
UR - http://www.scopus.com/inward/record.url?scp=85075636726&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85075636726&partnerID=8YFLogxK
U2 - 10.1109/TVCG.2019.2934260
DO - 10.1109/TVCG.2019.2934260
M3 - Article
C2 - 31403430
AN - SCOPUS:85075636726
VL - 26
SP - 492
EP - 502
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
SN - 1077-2626
IS - 1
M1 - 8794607
ER -