Augmented Reality (AR), as a novel data visualization tool, is advantageous in revealing spatial data patterns and data-context associations. Accordingly, recent research has identified AR data visualization as a promising approach to increasing decision-making efficiency and effectiveness. As a result, AR has been applied in various decision support systems to enhance knowledge conveying and comprehension, in which the different data-reality associations have been constructed to aid decision-making. However, how these AR visualization strategies can enhance different decision support datasets has not been reviewed thoroughly. Especially given the rise of Big Data in the modern world, this support is critical to decision-making in the coming years. Using AR to embed the decision support data and explanation data into the end user’s physical surroundings and focal contexts avoids isolating the human decision-maker from the relevant data. Integrating the decision-maker’s contexts and the DSS support in AR is a difficult challenge. This paper outlines the current state of the Art through a literature review in allowing AR data visualization to support decision-making. To facilitate the publication classification and analysis, the paper proposes one taxonomy to classify different AR data visualization based on the semantic associations between the AR data and physical context. Based on this taxonomy and a decision support system taxonomy, 37 publications have been classified and analyzed from multiple aspects. One of the contributions of this literature review is a resulting AR visualization taxonomy that can be applied to decision support systems. Along with this novel tool, the paper discusses the current state of Art in this field and indicates possible future challenges and directions that AR Data Visualization will bring to support decision-making.

Light Field Displays (LFD) offer the potential for a true window into a virtual world without any form of headset. However, the vast majority of LFD only provide motion parallax along the x-axis (horizontal) due to its domination of human 3D perception. The additional motion parallax along y and the z-axis are rarely or never achieved and let alone evaluated. This paper proposed the first approach that provided the real full-motion parallax covering the z-axis. Moreover, this generic approach enables on-demand y and z-axis motion parallax on any off-the-shelf LFD. A prototype was created according to the proposed approach, and with the use of it, an experiment with two tasks was carried out among 24 participants. This pioneering study explored the effect of the motion parallax along the additional axes. Subjective and objective metrics were collected to measure participants’ 3D perception on four viewing conditions (LFD with motion parallax along the x-axis, x-and-y-axis, x-and-z-axis, and x-y-and-z-axis). The study manifested three main findings: 1) The additional y-axis and z-axis motion parallax increased viewers’ engagement with the LFD. 2) LFD with full-motion parallax provided the optimal user experience. 3) The additional motion parallax along with the y-axis increased viewers’ user experience more than the z-axis.

Augmented Reality (AR) has shown significant potential in supporting in-situ decision-making in various application areas. Many prior works have shown how AR can visualize the decision support data in various contexts. However, prior research about AR-based decision support systems rarely explored how the explanations were visualized. Providing context-aware explanations within AR-based recommendation systems may help users instantly understand the recommendations they have been given. Therefore, this paper presents the world-first user study exploring AR explanation visualization designs. Three feature importance analysis visualizations that apply different color-coding and size-scaling strategies were designed to explain the recommendations provided by a context-aware AR shopping assistant system. Twenty-four participants were recruited to evaluate these three explanations in a shopping scenario. The results revealed novel findings that could help guide the appropriate utilization of descriptive parameters when designing AR explanation artifacts. The results also show the potential of providing intuitive visualization to explain recommendations in AR.

Augmented Reality and Virtual Reality become increasingly popular in scientific visualization especially for education where they can support collaborative scientific visualization experiences in the classroom. However, the inherent limitations of head-mounted AR and VR tools are stemming the popularization of these existing content sharing tools. Instead of sharing 3D educational content between AR/VR headsets, this paper proposes a novel prototype Mixed rEaliTy shAring pLatform (METAL) to allow for 3D educational content to be shared between a Microsoft HoloLens 2 and multiple Looking Glass displays which are a type of Light Field (Multi-view Autostereoscopic) display. This platform allows one teacher to use a HoloLens to manipulate and share different 3D contents with multiple student groups via the network, thus each student group can observe the synchronized 3D educational content with autostereoscopic experiences. Therefore, this proposed prototype enables a low-cost one-to-multiple 3D content sharing experience that allows the intuitive 3D model interaction and seamless communication between the students and the teacher.

As a promising alternative to VR head-mounted displays, current autostereoscopic displays and light field displays require high GPU consumption for multiple-view rendering and do not display different scenes for multiple viewers with motion parallax. Building upon prior work demonstrating how GPU utilization can be reduced by only rendering visible views, we propose an innovative approach to only render the visible views of different scenes towards multiple viewers’ eyes. Moreover, we found that the number of visible views decreases as the viewing distances increase. Thus, a dynamic approach can be taken to adjust the number of rendered views according to viewers distance from the display. This approach can be easily adapted to the off-the-shelf light field displays to display different 3D scenes for at least two viewers according to their head positions with reduced GPU costs.