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Matching and Reaching Depth Judgments with Real and Augmented Reality Targets
Abstract
Many compelling augmented reality (AR) applications require users to correctly perceive the location of virtual objects, some with accuracies as tight as 1 mm. However, measuring the perceived depth of AR objects at these accuracies has not yet been demonstrated. In this paper, we address this challenge by employing two different depth judgment methods, perceptual matching and blind reaching, in a series of three experiments, where observers judged the depth of real and AR target objects presented at reaching distances. Our experiments found that observers can accurately match the distance of a real target, but when viewing an AR target through collimating optics, their matches systematically overestimate the distance by 0.5 to 4.0 cm. However, these results can be explained by a model where the collimation causes the eyes' vergence angle to rotate outward by a constant angular amount. These findings give error bounds for using collimating AR displays at reaching distances, and suggest that for these applications, AR displays need to provide an adjustable focus. Our experiments further found that observers initially reach ∼4 cm too short, but reaching accuracy improves with both consistent proprioception and corrective visual feedback, and eventually becomes nearly as accurate as matching.
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“As depth information is important in manipulation tasks, we can infer that better performance in VR may in part be due to the superior information available when using VRHMDs.”
From Study of Multimodal Interfaces and the Improvements on Teleoperation (2020) · cites this paper as [21]
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“However, a signi cant difference was found in previous work [27].”
From Study of Multimodal Interfaces and the Improvements on Teleoperation (2020) · cites this paper as [27]
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Metadata
- Authors
- Swan, J. E., Singh, G., & Ellis, S. R.
- Affiliations
- Mississippi State University (US)
- Virginia Tech (US)
- Ames Research Center (US)
- Venue
- IEEE Transactions on Visualization and Computer Graphics
- Published
- 2015-11-15
- Volume
- 21
- Issue
- 11
- Pages
- 1289-1298
- ISSN
- 1077-2626 (ISSN-L)
- 1941-0506
- 2160-9306
- OpenAlex
- W1624742589
- Cited by
- 108
- References
- 35
- Citations by year
- 2026: 12 · 2025: 12 · 2024: 9 · 2023: 6 · 2022: 7 · 2021: 14 · 2020: 17 · 2019: 19 · 2018: 6 · 2017: 3 · 2016: 3
- FWCI
- 3.47
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Visualization and Computer Graphics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (IIS-0713609, IIS-1018413, IIS-1320909)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Augmented Reality Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Physical Sciences › Computer Science › Human-Computer Interaction › Interactive and Immersive Displays
- Keywords
- augmented reality, depth perception, perceptual matching, vergence angle, proprioception, visual feedback, reaching accuracy
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- Swan, J. E., Singh, G., & Ellis, S. R. (2015). Matching and Reaching Depth Judgments with Real and Augmented Reality Targets. IEEE Transactions on Visualization and Computer Graphics. https://doi.org/10.1109/tvcg.2015.2459895