TY - GEN

T1 - Modeling Pointing for 3D Target Selection in VR

AU - Dalsgaard, Tor-Salve

AU - Knibbe, Jarrod

AU - Bergström, Joanna

PY - 2021/12/8

Y1 - 2021/12/8

N2 - 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.

AB - 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.

KW - Faculty of Science

KW - Virtual reality

KW - pointing

KW - target selection

UR - http://dx.doi.org/10.1145/3489849.3489853

U2 - 10.1145/3489849.3489853

DO - 10.1145/3489849.3489853

M3 - Article in proceedings

SP - 1

EP - 10

BT - Proceedings of the 27th ACM Symposium on Virtual Reality Software and Technology

PB - Association for Computing Machinery

T2 - 27th ACM Symposium on Virtual Reality Software and Technology (VRST '21)

Y2 - 8 December 2021 through 10 December 2021

ER -