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|a 10.1109/TVCG.2024.3372077
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|a eng
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| 100 |
1 |
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|a Kruger, Marcel
|e verfasserin
|4 aut
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| 245 |
1 |
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|a IntenSelect+
|b Enhancing Score-Based Selection in Virtual Reality
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|c 2024
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|a Date Revised 22.04.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Object selection in virtual environments is one of the most common and recurring interaction tasks. Therefore, the used technique can critically influence a system's overall efficiency and usability. IntenSelect is a scoring-based selection-by-volume technique that was shown to offer improved selection performance over conventional raycasting in virtual reality. This initial method, however, is most pronounced for small spherical objects that converge to a point-like appearance only, is challenging to parameterize, and has inherent limitations in terms of flexibility. We present an enhanced version of IntenSelect called IntenSelect+ designed to overcome multiple shortcomings of the original IntenSelect approach. In an empirical within-subjects user study with 42 participants, we compared IntenSelect+ to IntenSelect and conventional raycasting on various complex object configurations motivated by prior work. In addition to replicating the previously shown benefits of IntenSelect over raycasting, our results demonstrate significant advantages of IntenSelect+ over IntenSelect regarding selection performance, task load, and user experience. We, therefore, conclude that IntenSelect+ is a promising enhancement of the original approach that enables faster, more precise, and more comfortable object selection in immersive virtual environments
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| 650 |
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|a Journal Article
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1 |
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|a Gerrits, Tim
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Romer, Timon
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kuhlen, Torsten
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Weissker, Tim
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g 30(2024), 5 vom: 22. Apr., Seite 2829-2838
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|g month:04
|g pages:2829-2838
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|u http://dx.doi.org/10.1109/TVCG.2024.3372077
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