Accuracy of Eyepoint Estimation in Optical See-Through Head-Mounted Displays Using the Single Point Active Alignment Method
2011 (English)Conference paper (Other academic)
This paper studies the accuracy of the estimated eyepoint of an Optical See-Through Head-Mounted Display (OST HMD) calibrated using the Single Point Active Alignment Method (SPAAM). Quantitative evaluation of calibration procedures for OST HMDs is complicated as it is currently not possible to share the subject’s view. Temporarily replacing the subject’s eye with a camera during the calibration or evaluation stage has been proposed, but the uncertainty of a correct eyepoint estimation remains. In the experiment reported in this paper, subjects were used for all stages of calibration and the results were verified with a 3D measurement device. The nine participants constructed 25 visual alignments per calibration after which the estimated pinhole camera model was decomposed into its intrinsic and extrinsic parameters using two common methods. Unique to this experiment, compared to previous evaluations, is the measurement device used to cup the subject’s eyeball. It measures the eyepoint location relative to the head tracker, thereby establishing the calibration accuracy of the estimated eyepoint location. As the results on accuracy are expressed as individual pinhole camera parameters, rather than a compounded registration error, this paper complements previously published work on parameter variance as the former denotes bias and the latter represents noise. Results indicate that the calibrated eyepoint is on average 5 cm away from its measured location and exhibits a vertical bias which potentially causes dipvergence for stereoscopic vision for objects located further away than 5.6 m. Lastly, this paper closes with a discussion on the suitability of the traditional pinhole camera model for OST HMD calibration.
Place, publisher, year, edition, pages
Accuracy, Single Point Active Alignment Method, Visual Alignment, Calibration, Augmented Reality
National CategoryEngineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-72054OAI: oai:DiVA.org:liu-72054DiVA: diva2:456340
IEEE Virtual Reality Conference 2012, Orange County (CA), USA