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On temporal-spatial realism in the virtual reality environment
Abstract
The Polhemus Isotrak is often used as an orientation and position tracking device in virtual reality environments. When it is used to dynamically determine the user's viewpoint and line of sight ( e.g. in the case of a head mounted display) the noise and delay in its measurement data causes temporal-spatial distortion, perceived by the user as jittering of images and lag between head movement and visual feedback. To tackle this problem, we first examined the major cause of the distortion, and found that the lag felt by the user is mainly due to the delay in orientation data, and the jittering of images is caused mostly by the noise in position data. Based on these observations, a predictive Kalman filter was designed to compensate for the delay in orientation data, and an anisotropic low pass filter was devised to reduce the noise in position data. The effectiveness and limitations of both approaches were then studied, and the results shown to be satisfactory. 1 Introduction In recen...
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Cited by
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Self-citationWare, C., Arthur, K., & Booth, K. S. (1993) · Proceedings of the SIGCHI conference on Human factors in computing systems - CHI '93 · doi:10.1145/169059.169066 · Cited by 353
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Optimal digital filtering for tremor suppression
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Metadata
- Authors
- Liang, J., Shaw, C., & Green, M.
- Venue
- Proceedings of the 4th annual ACM symposium on User interface software and technology - UIST '91
- Publisher
- ACM Press
- Published
- 1991
- Pages
- 19-25
- OpenAlex
- W1986255882
- Cited by
- 172
- References
- 0
- FWCI
- 43.74
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Liang, J., Shaw, C., & Green, M. (1991). On temporal-spatial realism in the virtual reality environment. Proceedings of the 4th annual ACM symposium on User interface software and technology - UIST '91. https://doi.org/10.1145/120782.120784
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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