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Interactive Extended Reality Techniques in Information Visualization

delete2022-12-01
delete9
PRE
AI
R
Richen Liu
M
Min Gao
L
Lijun Wang
X
Xiaohan Wang
Y
Yuzhe Xiang
A
Aolin Zhang
夏
夏佳志 (Jiazhi Xia)
Y
Yi Chen
陈
陈思明 (Siming Chen) *
DOI:10.1109/THMS.2022.3211317delete
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Abstract

Abstract

En 中文
Immersive techniques, such as virtual reality, augmented reality, and mixed reality, take immersive displays as carriers to provide immersive experience. A large number of approaches focus on the visualization of scientific data in immersive environments while just a few methods concentrate on interactive information visualization (InfoVis) in an immersive environment, although InfoVis has been extended to the 3-D space for a long time. In the era of data explosion, the traditional 2-D space is unable to convey large amounts of abstract information in an intuitive way. Meanwhile, desktop-based 3-D InfoVis generally leads to visual conflict and confusion owing to limited display size and field of vision. In this survey, we search for the interactive techniques in immersive InfoVis and summarize their commonalities and discuss their differences and potential trends. The data types of abstract information in InfoVis can be categorized into graph/network data, high-dimensional and multivariate data, time-varying data, and text and document data. Besides, the visual presentation of information in immersive environments is also summarized, especially for charts, plots, and diagrams, which are some basic components of InfoVis techniques. We also described the immersive applications of InfoVis techniques, including the tools or frameworks on immersive analytics and infographics. The discussion about the traditional nonimmersive and the immersive methods in data visualizations show that the latter one has the potential to become an alternative to explore massive information in the future.
Keywords:
Extended reality (XR)
immersive environment
information visualization (InfoVis)
virtual reality (VR)

Journal

IEEE Transactions on Human-Machine Systems cover
IEEE Transactions on Human-Machine Systems
IF:
4.4
Papers:
1.1K
Citations:
3.5K

Organization

F
fudan university
Scholars:
11.8W
Papers: 7.7W
Citations: 121
C
Central South University
Scholars:
10.0W
Papers: 7.2W
Citations: 10.9W
N
Nanjing Normal University
Scholars:
1.7W
Papers: 1.3W
Citations: 1.9W
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Cited Papers

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