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IEEE VR2023Top-tier venue

iARVis: Mobile AR Based Declarative Information Visualization Authoring, Exploring and Sharing

Junjie Chen, Chenhui Li, Sicheng Song, Changbo Wang

2023Year
5Citations

Abstract

We present iARVis, a proof-of-concept toolkit for creating, experiencing, and sharing mobile AR-based information visualization environments. Over the past years, AR has emerged as a promising medium for information and data visualization beyond the physical media and the desktop, enabling interactivity and eliminating spatial limits. However, the creation of such environments remains difficult and frequently necessitates low-level programming expertise and lengthy hand encodings. We present a declarative approach for defining the augmented reality (AR) environment, including how information is automatically positioned, laid out, and interacted with, to improve the efficiency and flexibility of constructing AR-based information visualization environments. We provide fundamental layout and visual components such as the grid, rich text, images, and charts for the development of complex visualization widgets, as well as automatic targeting methods based on image and object tracking for the development of the AR environment. To increase design efficiency, we also provide features such as hot-reload and several creation levels for both novice and advanced users. We also investigate how the augmented reality-based visualization environment could persist and be shared through the internet and provide ways for storing, sharing, and restoring the environment to give a continuous and seamless experience. To demonstrate the viability and extensibility, we evaluate iARVis using a variety of use cases along with performance evaluation and expert reviews.

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