Predictive tracking: Difference between revisions
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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>. | ||