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Predicting the gaze depth in head-mounted displays using multiple feature regression

  • 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).

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Metadaten
Document Type:Conference Object
Language:English
Parent Title (English):Sharif, Krejtz (Eds.): Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications (ETRA '18). Warsaw, Poland, June 14-17, 2018
Pagenumber:9
First Page:Article No. 19
ISBN:978-1-4503-5706-7
DOI:https://doi.org/10.1145/3204493.3204547
Publisher:ACM
Date of first publication:2018/06/14
Note:
The work is supported by the German Federal Ministry for Economic Affairs and Energy (BMWi), funding the MoVISO ZIM-project under Grant No.: ZF4120902
Tag:Eye Tracking; Gaze Depth Estimation; Virtual Reality
Departments, institutes and facilities:Fachbereich Informatik
Institute of Visual Computing (IVC)
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2018/07/25