Title

Immersive Exploration of Large Volume Vector Data

Presenting Author Name/s

Sean Leonard

Faculty Advisor

Zhanping Liu

Presentation Type

Poster

Disciplines

Other Computer Sciences

Description/Abstract

Flow (data) visualization plays a crucial role in a wide variety of areas such as oceanographic-atmospheric modeling (for studying ocean currents, hurricanes, tornados, and climate changes), computational fluid dynamics simulation (for designing aircrafts, space shuttles, and submarines), and diffusion tensor imaging analysis (for exploring numerous neural fibers of human brain and cardiovascular structures), to name only a few, by providing deep insight into the pattern underlying massive vector data. There have been many methods for visualizing flows ranging from steady to unsteady and from planar to surface and further to volume, while streamlines (i.e., field lines or flow lines that are point-wise tangent to the flow) remain a straightforward and efficient approach for investigating large steady flows. Dependent on seed positions, streamlines tend to result in either a coarse view or a cluttered image unless effective control is imposed. The latter case particularly incurs poor depth-cueing and view occlusion problems in 3D settings, overwhelming the user with an ocean of information instead of revealing high-level patterns and salient features. Streamline placement seeks to lay out streamlines in such a way that flow structures are sufficiently covered, but with visual cluttering and spatial ambiguity minimized, for ease of perception and understanding. In addition, virtual environments such as 4-wall CAVE, Microsoft HoloLens, HTC Vive, and Oculus Rift may be exploited to enhance the look and feel of a volumetric scene for realistic exploration and interactive analysis of data toward scientific discovery.

Session Title

Poster Session

Location

Learning Commons, Northwest Atrium

Start Date

2-2-2019 8:00 AM

End Date

2-2-2019 12:30 PM

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Feb 2nd, 8:00 AM Feb 2nd, 12:30 PM

Immersive Exploration of Large Volume Vector Data

Learning Commons, Northwest Atrium

Flow (data) visualization plays a crucial role in a wide variety of areas such as oceanographic-atmospheric modeling (for studying ocean currents, hurricanes, tornados, and climate changes), computational fluid dynamics simulation (for designing aircrafts, space shuttles, and submarines), and diffusion tensor imaging analysis (for exploring numerous neural fibers of human brain and cardiovascular structures), to name only a few, by providing deep insight into the pattern underlying massive vector data. There have been many methods for visualizing flows ranging from steady to unsteady and from planar to surface and further to volume, while streamlines (i.e., field lines or flow lines that are point-wise tangent to the flow) remain a straightforward and efficient approach for investigating large steady flows. Dependent on seed positions, streamlines tend to result in either a coarse view or a cluttered image unless effective control is imposed. The latter case particularly incurs poor depth-cueing and view occlusion problems in 3D settings, overwhelming the user with an ocean of information instead of revealing high-level patterns and salient features. Streamline placement seeks to lay out streamlines in such a way that flow structures are sufficiently covered, but with visual cluttering and spatial ambiguity minimized, for ease of perception and understanding. In addition, virtual environments such as 4-wall CAVE, Microsoft HoloLens, HTC Vive, and Oculus Rift may be exploited to enhance the look and feel of a volumetric scene for realistic exploration and interactive analysis of data toward scientific discovery.