TY  - CPAPER
U1  - Konferenzveröffentlichung
A1  - Weier, Martin
A1  - Roth, Thorsten
A1  - Hinkenjann, André
A1  - Slusallek, Philipp
T1  - Predicting the gaze depth in head-mounted displays using multiple feature regression
T2  - Sharif, Krejtz (Eds.): Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications (ETRA '18). Warsaw, Poland, June 14-17, 2018
N2  - Head-mounted displays (HMDs) with integrated eye trackers have opened up a new realm for gaze-contingent rendering. The accurate estimation of gaze depth is essential when modeling the optical capabilities of the eye. Most recently multifocal displays are gaining importance, requiring focus estimates to control displays or lenses. Deriving the gaze depth solely by sampling the scene's depth at the point-of-regard fails for complex or thin objects as eye tracking is suffering from inaccuracies. Gaze depth measures using the eye's vergence only provide an accurate depth estimate for the first meter. In this work, we combine vergence measures and multiple depth measures into feature sets. This data is used to train a regression model to deliver improved estimates. We present a study showing that using multiple features allows for an accurate estimation of the focused depth (MSE<0.1m) over a wide range (first 6m).
KW  - Eye Tracking
KW  - Gaze Depth Estimation
KW  - Virtual Reality
Y1  - 2018
SN  - 978-1-4503-5706-7
SB  - 978-1-4503-5706-7
U6  - https://doi.org/10.1145/3204493.3204547
DO  - https://doi.org/10.1145/3204493.3204547
SP  - 9
S1  - 9
PB  - Association for Computing Machinery
ER  -