Predictive tracking: Difference between revisions
Xinreality (talk | contribs) No edit summary |
Xinreality (talk | contribs) No edit summary |
||
| Line 2: | Line 2: | ||
{{see also|Tracking}} | {{see also|Tracking}} | ||
==Introduction== | ==Introduction== | ||
[[Predictive tracking]] is a fundamental technique used in both [[augmented reality]] (AR) and [[virtual reality]] (VR) systems that anticipates where a user's body parts or viewing direction will be in the near future. This computational method works by analyzing current motion patterns, velocity, and acceleration to estimate future positions before they occur<ref name="LaValle2016"></ref>. For example, when a VR game needs to display your virtual hand's position, it doesn't simply render where your hand currently | [[Predictive tracking]] is a fundamental technique used in both [[augmented reality]] (AR) and [[virtual reality]] (VR) systems that anticipates where a user's body parts or viewing direction will be in the near future. This computational method works by analyzing current motion patterns, velocity, and acceleration to estimate future positions before they occur<ref name="LaValle2016"></ref>. For example, when a VR game needs to display your virtual hand's position, it doesn't simply render where your hand currently is, it predicts where your hand will be several milliseconds in the future. | ||
The primary purpose of predictive tracking is to combat [[latency]] issues inherent in AR and VR systems. Without predictive algorithms, users would experience a noticeable delay between their physical movements and the corresponding visual feedback on their displays. This delay creates a disconnection that not only diminishes the sense of [[immersion]] but can also contribute to [[motion sickness]] and general discomfort<ref name="Abrash2014"></ref>. Through predictive tracking, the system estimates your future orientation and position based on your current input data, significantly reducing perceived latency and creating a more natural and responsive experience. | The primary purpose of predictive tracking is to combat [[latency]] issues inherent in AR and VR systems. Without predictive algorithms, users would experience a noticeable delay between their physical movements and the corresponding visual feedback on their displays. This delay creates a disconnection that not only diminishes the sense of [[immersion]] but can also contribute to [[motion sickness]] and general discomfort<ref name="Abrash2014"></ref>. Through predictive tracking, the system estimates your future orientation and position based on your current input data, significantly reducing perceived latency and creating a more natural and responsive experience. | ||
| Line 18: | Line 18: | ||
==Latency Sources== | ==Latency Sources== | ||
Understanding the sources of latency in AR and VR systems is crucial to implementing effective predictive tracking solutions. A specialized device known as a [[latency tester]] measures "motion-to-photon" latency within a | Understanding the sources of latency in AR and VR systems is crucial to implementing effective predictive tracking solutions. A specialized device known as a [[latency tester]] measures "motion-to-photon" latency within a headset, the time delay between physical movement and the corresponding visual update on the display. The longer this delay, the more uncomfortable and less immersive the experience becomes. | ||
Several distinct factors contribute to the overall system latency: | Several distinct factors contribute to the overall system latency: | ||
| Line 181: | Line 181: | ||
<ref name="Welch2002">Welch, G., & Foxlin, E. (2002). "Motion Tracking: No Silver Bullet, but a Respectable Arsenal." IEEE Computer Graphics and Applications, 22(6), pp. 24-38.</ref> | <ref name="Welch2002">Welch, G., & Foxlin, E. (2002). "Motion Tracking: No Silver Bullet, but a Respectable Arsenal." IEEE Computer Graphics and Applications, 22(6), pp. 24-38.</ref> | ||
<ref name="Welch2006">Welch, G., & Bishop, G. (2006). "An Introduction to the Kalman Filter." University of North Carolina at Chapel Hill, Department of Computer Science, Technical Report 95-041.</ref> | <ref name="Welch2006">Welch, G., & Bishop, G. (2006). "An Introduction to the Kalman Filter." University of North Carolina at Chapel Hill, Department of Computer Science, Technical Report 95-041.</ref> | ||
<ref name="Isard1998">Isard, M., & Blake, A. (1998). " | <ref name="Isard1998">Isard, M., & Blake, A. (1998). "CONDENSATION-Conditional Density Propagation for Visual Tracking." International Journal of Computer Vision, 29(1), pp. 5-28.</ref> | ||
<ref name="LaViola2003">LaViola, J. J. (2003). "Double Exponential Smoothing: An Alternative to Kalman Filter-Based Predictive Tracking." Proceedings of the Workshop on Virtual Environments, pp. 199-206.</ref> | <ref name="LaViola2003">LaViola, J. J. (2003). "Double Exponential Smoothing: An Alternative to Kalman Filter-Based Predictive Tracking." Proceedings of the Workshop on Virtual Environments, pp. 199-206.</ref> | ||
<ref name="Greer2020">Greer, J., & Johnson, K. (2020). "Multi-modal Prediction for XR Tracking." IEEE Conference on Virtual Reality and 3D User Interfaces (VR), pp. 161-170.</ref> | <ref name="Greer2020">Greer, J., & Johnson, K. (2020). "Multi-modal Prediction for XR Tracking." IEEE Conference on Virtual Reality and 3D User Interfaces (VR), pp. 161-170.</ref> | ||