TY - GEN
T1 - Control with Vergence Eye Movement in Augmented Reality See-Through Vision
AU - Wang, Zhimin
AU - Zhao, Yuxin
AU - Lu, Feng
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Augmented Reality (AR) see-through vision has become a recent research focus since it enables the user to see through a wall and see the occluded objects. Most existing works only used common modalities to control the display for see-through vision, e.g., button clicking and speech control. However, we use visual system to observe see-through vision. Using an addition interaction channel will distract the user and degrade the user experience. In this paper, we propose a novel interaction method using vergence eye movement for controlling see-through vision in AR. Specifically, we first customize eye cameras and design gaze depth estimation method for Microsoft HoloLens 2. With our algorithm, fixation depth can be computed from the vergence, and used to manage the see-through vision. We also propose two control techniques of gaze vergence. The experimental results show that the gaze depth estimation method is efficient. The difference cannot be found between these two modalities in terms of completion time and the number of successes.
AB - Augmented Reality (AR) see-through vision has become a recent research focus since it enables the user to see through a wall and see the occluded objects. Most existing works only used common modalities to control the display for see-through vision, e.g., button clicking and speech control. However, we use visual system to observe see-through vision. Using an addition interaction channel will distract the user and degrade the user experience. In this paper, we propose a novel interaction method using vergence eye movement for controlling see-through vision in AR. Specifically, we first customize eye cameras and design gaze depth estimation method for Microsoft HoloLens 2. With our algorithm, fixation depth can be computed from the vergence, and used to manage the see-through vision. We also propose two control techniques of gaze vergence. The experimental results show that the gaze depth estimation method is efficient. The difference cannot be found between these two modalities in terms of completion time and the number of successes.
KW - Augmented Reality
KW - Human Computer Interaction (HCI)
KW - See-through Vision
KW - Vergence Eye Movement
UR - https://www.scopus.com/pages/publications/85129685772
U2 - 10.1109/VRW55335.2022.00125
DO - 10.1109/VRW55335.2022.00125
M3 - 会议稿件
AN - SCOPUS:85129685772
T3 - Proceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2022
SP - 548
EP - 549
BT - Proceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2022
Y2 - 12 March 2022 through 16 March 2022
ER -