Title
Adaptive information density for augmented reality displays
Abstract
Augmented Reality (AR) browsers show geo-referenced data in the current view of a user. When the amount of data grows too large, the display quickly becomes cluttered. Clustering items by spatial and semantic attributes can temporarily alleviate the issue, but is not effective against an increasing amount of data. We present an adaptive information density display for AR that balances the amount of presented information against the potential clutter created by placing items on the screen. We use hierarchical clustering to create a level-of-detail structure, in which nodes closer to the root encompass groups of items, while the leaf nodes contain single items. Our method selects items and groups from different levels of this hierarchy based on user-defined preferences and on the amount of visual clutter caused by placing these items. The number of presented items is adapted during user interaction to avoid clutter. We compare our interface to a conventional AR browser interface in a qualitative user study. Users clearly preferred our interface, because it provided a better overview of the data and allowed for easier comparison. In a second study, we evaluated the effect of different degrees of clustering on search and recall tasks. Users generally made fewer errors, when using our interface for a search task, which indicates that the reduced clutter allowed them to stay focused on finding the relevant items.
Year
DOI
Venue
2016
10.1109/VR.2016.7504691
2016 IEEE Virtual Reality (VR)
Keywords
Field
DocType
adaptive information density display,augmented reality displays,AR browsers,geo-referenced data,items clustering,spatial attributes,semantic attributes,hierarchical clustering,level-of-detail structure,leaf nodes,user-defined preferences,visual clutter,AR browser interface,search-recall tasks
Hierarchical clustering,Computer vision,Data visualization,Computer science,Clutter,Visualization,Augmented reality,Artificial intelligence,Hierarchy,Cluster analysis,Semantics
Conference
ISSN
Citations 
PageRank 
1087-8270
5
0.46
References 
Authors
17
6
Name
Order
Citations
PageRank
Markus Tatzgern11068.43
Valeria Orso26612.23
Kalkofen, Denis335128.66
Giulio Jacucci41701126.44
Luciano Gamberini536849.76
Dieter Schmalstieg64169332.77