Subjective Evaluation of a Semi-Automatic Optical See-Through Head-Mounted Display Calibration Technique

IEEE Trans Vis Comput Graph. 2015 Apr;21(4):491-500. doi: 10.1109/TVCG.2015.2391856.

Abstract

With the growing availability of optical see-through (OST) head-mounted displays (HMDs) there is a present need for robust, uncomplicated, and automatic calibration methods suited for non-expert users. This work presents the results of a user study which both objectively and subjectively examines registration accuracy produced by three OST HMD calibration methods: (1) SPAAM, (2) Degraded SPAAM, and (3) Recycled INDICA, a recently developed semi-automatic calibration method. Accuracy metrics used for evaluation include subject provided quality values and error between perceived and absolute registration coordinates. Our results show all three calibration methods produce very accurate registration in the horizontal direction but caused subjects to perceive the distance of virtual objects to be closer than intended. Surprisingly, the semi-automatic calibration method produced more accurate registration vertically and in perceived object distance overall. User assessed quality values were also the highest for Recycled INDICA, particularly when objects were shown at distance. The results of this study confirm that Recycled INDICA is capable of producing equal or superior on-screen registration compared to common OST HMD calibration methods. We also identify a potential hazard in using reprojection error as a quantitative analysis technique to predict registration accuracy. We conclude with discussing the further need for examining INDICA calibration in binocular HMD systems, and the present possibility for creation of a closed-loop continuous calibration method for OST Augmented Reality.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Adult
  • Algorithms
  • Analysis of Variance
  • Calibration
  • Computer Graphics / instrumentation*
  • Equipment Design
  • Eye Movements / physiology*
  • Female
  • Humans
  • Male
  • User-Computer Interface*
  • Young Adult