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abstract

Computing Object Selection Difficulty in VR using Run-time Contextual Analysis

Published: 01 November 2020 Publication History

Abstract

This paper introduces a method for computing the difficulty of selection tasks in virtual environments using pointing metaphors by operationalizing an established human motor behavior model. In contrast to previous work, the difficulty is calculated automatically at run-time for arbitrary environments. We present and provide the implementation of our method within Unity 3D. The difficulty is computed based on a contextual analysis of spatial boundary conditions, i.e., target object size and shape, distance to the user, and occlusion. We believe our method will enable developers to build adaptive systems that automatically equip the user with the most appropriate selection technique according to the context. Further, it provides a standard metric to better evaluate and compare different selection techniques.

References

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Ferran Argelaguet and Carlos Andujar. 2009. Efficient 3D pointing selection in cluttered virtual environments. IEEE Computer Graphics and Applications 29, 6 (2009), 34–43.
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Ferran Argelaguet and Carlos Andujar. 2013. A survey of 3D object selection techniques for virtual environments. Computers & Graphics 37, 3 (2013), 121–136.
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Doug A. Bowman, Brian Badillo, and Dhruv Manek. 2007. Evaluating the Need for Display-specific and Device-specific 3D Interaction Techniques. In Proceedings of the 2Nd International Conference on Virtual Reality (Beijing, China) (ICVR’07). Springer-Verlag, Berlin, Heidelberg, 195–204. http://dl.acm.org/citation.cfm?id=1770090.1770114
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Doug A. Bowman, Ernst Kruijff, Joseph J. LaViola, and Ivan Poupyrev. 2004. 3D User Interfaces: Theory and Practice. Addison Wesley Longman Publishing Co., Inc., Redwood City, CA, USA.
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Jeffrey Cashion, Chadwick Wingrave, and Joseph J LaViola. 2013. Optimal 3D selection technique assignment using real-time contextual analysis. In 3D User Interfaces (3DUI), 2013 IEEE Symposium on. IEEE, 107–110.
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Jeffrey Cashion, Chadwick Wingrave, and Joseph J LaViola Jr. 2012. Dense and dynamic 3d selection for game-based virtual environments. IEEE transactions on visualization and computer graphics 18, 4(2012), 634–642.
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Niklas Elmqvist and Philippas Tsigas. 2008. A taxonomy of 3d occlusion management for visualization. IEEE Transactions on Visualization and Computer Graphics 14, 5(2008), 1095–1109.
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Enox Software. 2019. OpenCV for Unity. https://assetstore.unity.com/detail/tools/integration/opencv-for-unity-21088
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Joseph J. LaViola Jr.2017. 3D User Interfaces: Theory and Practice (2nd Edition). Addison-Wesley Professional. https://www.xarg.org/ref/a/0134034325/
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Regis Kopper, Doug A. Bowman, Mara G. Silva, and Ryan P. McMahan. 2010. A Human Motor Behavior Model for Distal Pointing Tasks. Int. J. Hum.-Comput. Stud. 68, 10 (Oct. 2010), 603–615. https://doi.org/10.1016/j.ijhcs.2010.05.001
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T. Pfeiffer and M. E. Latoschik. 2004. Resolving object references in multimodal dialogues for immersive virtual environments. In IEEE Virtual Reality 2004. 35–277. https://doi.org/10.1109/VR.2004.1310053
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cover image ACM Conferences
VRST '20: Proceedings of the 26th ACM Symposium on Virtual Reality Software and Technology
November 2020
429 pages
ISBN:9781450376198
DOI:10.1145/3385956
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 01 November 2020

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  1. 3DUI
  2. Application
  3. Performance Model
  4. Virtual Reality

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