THESIS
2020
xx, 143 pages : illustrations ; 30 cm
Abstract
An increasingly large amount of data from the physical world has been collected, digitized,
and stored. To communicate such data to general public effectively, visual data-driven
storytelling has been widely used. Yet, the vast majority of data communication
occurs on desktop computers separated from the physical world the data originates in and
refers to. Recent advances in Augmented Reality (AR) have shed new light on data-driven
storytelling, offering exciting possibilities for telling engaging, in-situ, and immersive stories
by embedding the data in the real-world context. However, although creating such
kind of AR data stories is demanding and requires considerable knowledge and skills
from different fields (e.g., data visualization, computer graphics, computer vision, and...[
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An increasingly large amount of data from the physical world has been collected, digitized,
and stored. To communicate such data to general public effectively, visual data-driven
storytelling has been widely used. Yet, the vast majority of data communication
occurs on desktop computers separated from the physical world the data originates in and
refers to. Recent advances in Augmented Reality (AR) have shed new light on data-driven
storytelling, offering exciting possibilities for telling engaging, in-situ, and immersive stories
by embedding the data in the real-world context. However, although creating such
kind of AR data stories is demanding and requires considerable knowledge and skills
from different fields (e.g., data visualization, computer graphics, computer vision, and
human-machine interaction), prior research has rarely investigated the effective way to
create visual data-driven stories in AR environments.
This thesis aims to fill this gap by exploring the approaches to facilitate the authoring
of infographics, a popular format for data-driven storytelling, in AR. Given that AR
devices are still evolving rapidly, this thesis focuses on the interaction between reality
and virtuality, an essence of AR that is independent of specific devices. The first system,
MARVisT, leverages physical properties (e.g., size, positions) from real-world objects to
assist non-experts in creating 3D infographics in mobile AR. Given the limited interaction
capabilities of mobile devices, in the second work, LassoNet is proposed to facilitate
the selection of 3D objects in a 2D screen based on a deep neural network. Besides augmenting
the physical world, the third research studies augmenting the semantic content
of real-world infographics in AR and introduces PapARVis Designer to allow designers
to create augmented static visualizations. Finally, the fourth work explores automating
the creation of timeline infographics in AR by developing a deep-learning based method
that automatically extracts extensible templates from real timeline infographics to generate
virtual timelines with new data in AR.
The core idea of this thesis is to allow visualization designers to go beyond the desktop
platform to the engaging, immersive, and promising AR platform that is seen to be
the next-generation human-machine interaction platform. The resulting systems and techniques
blaze a trail toward futuristic visual storytelling.
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