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UIST '91 Proceedings of the 4th annual ACM symposium on User interface software and technology
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I3D '92 Proceedings of the 1992 symposium on Interactive 3D graphics
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SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
A 3D tracking experiment on latency and its compensation methods in virtual environments
Proceedings of the 8th annual ACM symposium on User interface and software technology
A frequency-domain analysis of head-motion prediction
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
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An introduction to support Vector Machines: and other kernel-based learning methods
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I3D '01 Proceedings of the 2001 symposium on Interactive 3D graphics
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Proceedings of the conference on Visualization '01
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VR '02 Proceedings of the IEEE Virtual Reality Conference 2002
An Introduction to the Kalman Filter
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Predictive head movement tracking using a Kalman filter
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Virtual reality system with integrated sound field simulation and reproduction
EURASIP Journal on Applied Signal Processing
Towards an information model of consistency maintenance in distributed interactive applications
International Journal of Computer Games Technology - Networking for Computer Games
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We present a testbed for comparing predictive tracking algorithms that allows virtual environment system developers to make better choices about which predictors to use in their environments and aids researchers in determining how predictors work across various virtual environment configurations. Our testbed saves the virtual environment developer and researcher both time and effort with the important task of reducing dynamic tracking error and masking latency. The testbed consists of three components: a prediction algorithm library, a motion data repository, and a graphical testing application which provides users with the ability to test different predictive tracking algorithms across a variety of user motion sequences. The testbed provides enough generality for testing across different algorithmic and system parameters such as sampling rate, prediction time, and noise variance. The paper describes the contents of the predictor library and how to extend it, the types of motion data sets collected thus far, the motion data preparation methodology, and the graphical testing application's functionality and architecture. A simple testing scenario showing output from the testbed is also presented.