Complementary space for enhanced uncertainty and dynamics visualization

Chandrajit Bajaj; Andrew Gillette; Samrat Goswami; Bong June Kwon; Jose Rivera

doi:10.1007/978-3-642-15014-2_18

Complementary space for enhanced uncertainty and dynamics visualization

Chandrajit Bajaj, Andrew Gillette, Samrat Goswami, Bong June Kwon, Jose Rivera

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

Many computational modeling pipelines for geometry processing and visualization focus on topologically and geometrically accurate shape reconstruction of “primal” space, meaning the surface of interest and the volume it contains. Certain features of a surface such as pockets, tunnels, and voids (small, closed components) often represent important properties of the model and yet are difficult to detect or visualize in a model of primal space alone. It is natural, then, to consider what information can be gained from a model and visualization of complementary space, i.e. the space exterior to but still “near” the surface in question. In this paper, we show how complementary space can be used as a tool for both uncertainty and dynamics visualizations and analysis.

Original language	English (US)
Title of host publication	Mathematics and Visualization
Editors	Valerio Pascucci, Xavier Tricoche, Hans Hagen, Julien Tierny
Publisher	Springer Heidelberg
Pages	217-228
Number of pages	12
ISBN (Electronic)	9783642150142
ISBN (Print)	9783319912738, 9783540250326, 9783540250760, 9783540332749, 9783540886051, 9783642150135, 9783642150135, 9783642216077, 9783642231742, 9783642273421, 9783642341403, 9783642543005
DOIs	https://doi.org/10.1007/978-3-642-15014-2_18
State	Published - 2011
Externally published	Yes
Event	3rd Workshop on Topological Methods in Data Analysis and Visualization, TopoInVis 2009 - Snowbird, United States Duration: Feb 23 2009 → Feb 24 2009

Publication series

Name	Mathematics and Visualization
Volume	0
ISSN (Print)	1612-3786
ISSN (Electronic)	2197-666X

Conference

Conference	3rd Workshop on Topological Methods in Data Analysis and Visualization, TopoInVis 2009
Country/Territory	United States
City	Snowbird
Period	2/23/09 → 2/24/09

ASJC Scopus subject areas

Modeling and Simulation
Geometry and Topology
Computer Graphics and Computer-Aided Design
Applied Mathematics

Access to Document

10.1007/978-3-642-15014-2_18

Cite this

Complementary space for enhanced uncertainty and dynamics visualization. / Bajaj, Chandrajit; Gillette, Andrew; Goswami, Samrat et al.
Mathematics and Visualization. ed. / Valerio Pascucci; Xavier Tricoche; Hans Hagen; Julien Tierny. Springer Heidelberg, 2011. p. 217-228 (Mathematics and Visualization; Vol. 0).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Bajaj, C, Gillette, A, Goswami, S, Kwon, BJ & Rivera, J 2011, Complementary space for enhanced uncertainty and dynamics visualization. in V Pascucci, X Tricoche, H Hagen & J Tierny (eds), Mathematics and Visualization. Mathematics and Visualization, vol. 0, Springer Heidelberg, pp. 217-228, 3rd Workshop on Topological Methods in Data Analysis and Visualization, TopoInVis 2009, Snowbird, United States, 2/23/09. https://doi.org/10.1007/978-3-642-15014-2_18

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abstract = "Many computational modeling pipelines for geometry processing and visualization focus on topologically and geometrically accurate shape reconstruction of “primal” space, meaning the surface of interest and the volume it contains. Certain features of a surface such as pockets, tunnels, and voids (small, closed components) often represent important properties of the model and yet are difficult to detect or visualize in a model of primal space alone. It is natural, then, to consider what information can be gained from a model and visualization of complementary space, i.e. the space exterior to but still “near” the surface in question. In this paper, we show how complementary space can be used as a tool for both uncertainty and dynamics visualizations and analysis.",

author = "Chandrajit Bajaj and Andrew Gillette and Samrat Goswami and Kwon, {Bong June} and Jose Rivera",

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AB - Many computational modeling pipelines for geometry processing and visualization focus on topologically and geometrically accurate shape reconstruction of “primal” space, meaning the surface of interest and the volume it contains. Certain features of a surface such as pockets, tunnels, and voids (small, closed components) often represent important properties of the model and yet are difficult to detect or visualize in a model of primal space alone. It is natural, then, to consider what information can be gained from a model and visualization of complementary space, i.e. the space exterior to but still “near” the surface in question. In this paper, we show how complementary space can be used as a tool for both uncertainty and dynamics visualizations and analysis.

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Complementary space for enhanced uncertainty and dynamics visualization

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