MARVisT: Authoring Glyph-based Visualization in Mobile Augmented Reality
Chen Zhu-Tian, Yijia Su, Yifang Wang, Qianwen Wang, Huamin Qu, Yingcai Wu
TL;DR
MARVisT addresses the challenge of enabling non-experts to author glyph-based visualizations in mobile AR by introducing a bottom-up, data-driven AR visualization authoring tool tailored for in-situ use. It formalizes ARGVis design through four principles (DC1–DC4) and implements context-aware nudging, visual scale synchronization, and auto-layout to facilitate rapid, expressive AR infographics. Through a gallery of examples and a user study with non-experts, the work demonstrates high usability, strong engagement, and practical usefulness for personal visualization in context-rich environments. The findings suggest that mobile AR can effectively support personal data communication, with MARVisT serving as a viable framework and stepping stone toward broader AR visualization authoring on consumer devices.
Abstract
Recent advances in mobile augmented reality (AR) techniques have shed new light on personal visualization for their advantages of fitting visualization within personal routines, situating visualization in a real-world context, and arousing users' interests. However, enabling non-experts to create data visualization in mobile AR environments is challenging given the lack of tools that allow in-situ design while supporting the binding of data to AR content. Most existing AR authoring tools require working on personal computers or manually creating each virtual object and modifying its visual attributes. We systematically study this issue by identifying the specificity of AR glyph-based visualization authoring tool and distill four design considerations. Following these design considerations, we design and implement MARVisT, a mobile authoring tool that leverages information from reality to assist non-experts in addressing relationships between data and virtual glyphs, real objects and virtual glyphs, and real objects and data. With MARVisT, users without visualization expertise can bind data to real-world objects to create expressive AR glyph-based visualizations rapidly and effortlessly, reshaping the representation of the real world with data. We use several examples to demonstrate the expressiveness of MARVisT. A user study with non-experts is also conducted to evaluate the authoring experience of MARVisT.
