Predictive tracking: Difference between revisions
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{{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. | ||
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==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: | ||
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*'''Dynamic Resolution Scaling''' - To maintain frame rates critical for effective predictive tracking, many systems dynamically adjust rendering resolution based on scene complexity and current performance metrics. This technique ensures consistent frame timing, which is essential for predictive algorithms that depend on regular update intervals<ref name="Patney2016"></ref>. | *'''Dynamic Resolution Scaling''' - To maintain frame rates critical for effective predictive tracking, many systems dynamically adjust rendering resolution based on scene complexity and current performance metrics. This technique ensures consistent frame timing, which is essential for predictive algorithms that depend on regular update intervals<ref name="Patney2016"></ref>. | ||
*'''Sensor Fusion''' - Before prediction occurs, raw sensor data must be combined through sensor fusion techniques. These approaches merge data from complementary sensors ( | *'''Sensor Fusion''' - Before prediction occurs, raw sensor data must be combined through sensor fusion techniques. These approaches merge data from complementary sensors (for example combining gyroscope data with camera-based tracking) to create a more accurate representation of current position and orientation. The quality of this fusion directly impacts prediction accuracy<ref name="Foxlin1996"></ref>. | ||
*'''Simultaneous Localization and Mapping (SLAM)''' - In AR systems and inside-out tracking VR headsets, SLAM techniques construct and maintain maps of the surrounding environment. While SLAM primarily focuses on determining current position rather than predicting future positions, these maps provide valuable contextual information that can constrain and improve predictions<ref name="Davison2007"></ref>. | *'''Simultaneous Localization and Mapping (SLAM)''' - In AR systems and inside-out tracking VR headsets, SLAM techniques construct and maintain maps of the surrounding environment. While SLAM primarily focuses on determining current position rather than predicting future positions, these maps provide valuable contextual information that can constrain and improve predictions<ref name="Davison2007"></ref>. | ||
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<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> | ||