Comparison of visual highlighting techniques for complex and high-fidelity tasks in VR
Date
2025
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In unserem Alltag suchen wir regelmäßig nach Dingen. Oft suchen wir dabei unsere Umgebung visuell ab. In anderen Situationen versuchen Schilder unsere Aufmerksamkeit zu erregen. Ein Augmented Reality (AR)-basierter Assistent, der wichtige Objekte visuell hervorhebt würde sich daher in einer Vielzahl von Situationen als nützlich erweisen. Aber wie lenken wir am besten die Aufmerksamkeit von Nutzern? Auf 2D Bildschirmen mit limitierter Größe reichen dafür meistens einfache Ansätze wie das Einfärben von Objekten. Allerdings ist das in Augmented und Virtual Reality (VR) schwieriger, da Ziele in jeder Richtung liegen können, was das Hervorheben erschwert. Es wurden bereits viele Hervorhebungstechniken für den Gebrauch in AR und VR vorgestellt, um dieses Problem zu lösen. Da AR Hardware immer noch Limitationen aufweist, werden Techniken häufig in VR evaluiert. Jedoch werden diese Auswertungen oft in abstrakten, kleinen oder statischen Umgebungen durchgeführt. Das Ziel dieser Masterarbeit ist es, einen Einblick in den Effekt von Hervorhebungstechniken in einer komplexen und realitätsnahen Umgebung, welche Nutzer naturgemäß durchqueren, zu geben. Zu diesem Zweck haben wir bereits existierende Ansätze analysiert. Wir haben vier Hervorhebungstechniken (Color + Outline, Link, Arrow und HiveFive) in VR implementiert. Weiterhin haben wir eine realitätsnahe virtuelle Innenstadt mit verschiedenen dynamischen Elementen entwickelt, in welcher die Hervorhebungstechniken verglichen werden. Mit Hilfe einer Omnidirectional Treadmill ist es möglich, diese Umgebung natürlich laufend zu durchqueren. Die Ergebnisse unserer Studie haben gezeigt, dass in unserer realitätsnahen Umgebung Link die schnellste und Arrow die langsamste Hervorhebungstechnik war. Außerdem hatten Color + Outline und HiveFive eine erhöhte Rate von verpassen Zielen. In Bezug auf persönliche Preferenz zeigte sich, dass Arrow am schlechtesten abschnitt, während die anderen Drei eher beliebt waren. Beim visuellen Hervorheben drückten die Teilnehmer außerdem eine Präferenz für eine Kombination von in-view und in-situ Elementen aus. All dies deutet darauf hin, dass verschiedene Hervorhebungstechniken verschieden geeignet sind, in komplexen und realitätsnahen Umgebungen Objekte zu finden.
During our everyday lives, we regularly look for things, performing visual searches. At other times, signs seek to attract our attention. An Augmented Reality (AR)-based assistant system visually highlighting objects of interest would thus prove useful in a variety of situations. But how do we best guide users’ attention? On 2D displays with limited size, simple approaches such as coloring are often sufficient. However, in Augmented and Virtual Reality (VR), targets can be located in any direction, making highlighting more difficult. Many highlighting techniques for use in AR and VR have been proposed to solve this issue. As AR hardware still has some limitations, techniques are frequently evaluated in VR. Yet, these evaluations are often conducted in abstract, small, or static settings. This thesis aims to provide insight into the performance of highlighting techniques in a complex and high-fidelity environment, which users naturally traverse. For this, we analyzed previously proposed approaches. In VR, we implemented four highlighting techniques to compare (Color + Outline, Link, Arrow, and HiveFive). We further provided a high-fidelity virtual city center with various dynamic elements in which to compare the highlighting techniques. Using an Omnidirectional Treadmill, it is possible to traverse this environment while walking naturally. Results of our user study showed that in our high-fidelity environment, Link was the fastest highlighting technique, while Arrow was the slowest. Further, Color + Outline and HiveFive had an increased rate of missed targets. Regarding personal preference, Arrow performed the worst, while the other three were rather popular. Participants also expressed a preference for a combination of in-view and in-situ elements for visual highlighting. This suggests that different highlighting techniques are differently suited for finding objects in complex and high-fidelity environments.
During our everyday lives, we regularly look for things, performing visual searches. At other times, signs seek to attract our attention. An Augmented Reality (AR)-based assistant system visually highlighting objects of interest would thus prove useful in a variety of situations. But how do we best guide users’ attention? On 2D displays with limited size, simple approaches such as coloring are often sufficient. However, in Augmented and Virtual Reality (VR), targets can be located in any direction, making highlighting more difficult. Many highlighting techniques for use in AR and VR have been proposed to solve this issue. As AR hardware still has some limitations, techniques are frequently evaluated in VR. Yet, these evaluations are often conducted in abstract, small, or static settings. This thesis aims to provide insight into the performance of highlighting techniques in a complex and high-fidelity environment, which users naturally traverse. For this, we analyzed previously proposed approaches. In VR, we implemented four highlighting techniques to compare (Color + Outline, Link, Arrow, and HiveFive). We further provided a high-fidelity virtual city center with various dynamic elements in which to compare the highlighting techniques. Using an Omnidirectional Treadmill, it is possible to traverse this environment while walking naturally. Results of our user study showed that in our high-fidelity environment, Link was the fastest highlighting technique, while Arrow was the slowest. Further, Color + Outline and HiveFive had an increased rate of missed targets. Regarding personal preference, Arrow performed the worst, while the other three were rather popular. Participants also expressed a preference for a combination of in-view and in-situ elements for visual highlighting. This suggests that different highlighting techniques are differently suited for finding objects in complex and high-fidelity environments.