Modeling Pointing for 3D Target Selection in VR

Tor-Salve Dalsgaard*, Jarrod Knibbe, Joanna Bergström

*Corresponding author af dette arbejde

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review

12 Citationer (Scopus)
85 Downloads (Pure)

Abstract

Virtual reality (VR) allows users to interact similarly to how they do in the physical world, such as touching, moving, and pointing at objects. To select objects at a distance, most VR techniques rely on casting a ray through one or two points located on the user’s body (e.g., on the head and a finger), and placing a cursor on that ray. However, previous studies show that such rays do not help users achieve optimal pointing accuracy nor correspond to how they would naturally point. We seek to find features, which would best describe natural pointing at distant targets. We collect motion data from seven locations on the hand, arm, and body, while participants point at 27 targets across a virtual room. We evaluate the features of pointing and analyse sets of those for predicting pointing targets. Our analysis shows an 87% classification accuracy between the 27 targets for the best feature set and a mean distance of 23.56 cm in predicting pointing targets across the room. The feature sets can inform the design of more natural and effective VR pointing techniques for distant object selection.
OriginalsprogEngelsk
TitelProceedings of the 27th ACM Symposium on Virtual Reality Software and Technology
ForlagAssociation for Computing Machinery
Publikationsdato8 dec. 2021
Sider1-10
Artikelnummer42
ISBN (Elektronisk)978-1-4503-9092-7
DOI
StatusUdgivet - 8 dec. 2021
Begivenhed27th ACM Symposium on Virtual Reality Software and Technology (VRST '21) - Osaka, Japan
Varighed: 8 dec. 202110 dec. 2021

Konference

Konference27th ACM Symposium on Virtual Reality Software and Technology (VRST '21)
Land/OmrådeJapan
ByOsaka
Periode08/12/202110/12/2021

Emneord

  • Det Natur- og Biovidenskabelige Fakultet

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