A Perceptual Matching Technique for Depth Judgments in Optical, See-Through Augmented Reality

  • Authors:
  • J. Edward II Swan;Mark A. Livingston;Harvey S. Smallman;Dennis Brown;Yohan Baillot;Joseph L. Gabbard;Deborah Hix

  • Affiliations:
  • Mississippi State University;Naval Research Laboratory;Pacific Science & Engineering Group;Naval Research Laboratory;Advanced Engineering and Science ITT Industries;Systems Research Center, Virginia Tech;Systems Research Center, Virginia Tech

  • Venue:
  • VR '06 Proceedings of the IEEE conference on Virtual Reality
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

A fundamental problem in optical, see-through augmented reality (AR) is characterizing how it affects the perception of spatial layout and depth. This problem is important because AR system developers need to both place graphics in arbitrary spatial relationships with real-world objects, and to know that users will perceive them in the same relationships. Furthermore, AR makes possible enhanced perceptual techniques that have no real-world equivalent, such as x-ray vision, where AR users are supposed to perceive graphics as being located behind opaque surfaces. This paper reviews and discusses techniques for measuring egocentric depth judgments in both virtual and augmented environments. It then describes a perceptual matching task and experimental design for measuring egocentric AR depth judgments at medium- and far-field distances of 5 to 45 meters. The experiment studied the effect of field of view, the x-ray vision condition, multiple distances, and practice on the task. The paper relates some of the findings to the well-known problem of depth underestimation in virtual environments, and further reports evidence for a switch in bias, from underestimating to overestimating the distance of AR-presented graphics, at 23 meters. It also gives a quantification of how much more difficult the x-ray vision condition makes the task, and then concludes with ideas for improving the experimental methodology.