Adaptive Multi-Resolution Rendering for Virtual Reality Scenes: A Dynamic Resolution and Task Scheduling Approach
Abstract
This paper presents an adaptive multi-resolution rendering optimization algorithm for virtual reality (VR) environments based on real-time eye-tracking and dynamic model complexity analysis. The proposed method integrates three key modules: a visual focus detection component using a 2D Gaussian sensitivity function centered on the gaze point, a model importance evaluation mechanism based on texture density and triangle count, and a resolution adjustment scheduler that assigns level-of-detail (LOD) dynamically. To validate performance, comparative experiments were conducted against two baseline methods: fixed-resolution rendering and heuristic rule-based adaptation. Results demonstrate a consistent frame rate improvement from 81 fps to 121 fps (p<0.01), while maintaining subjective visual quality above 7.8 on a 10-point scale. Standard deviation remained within ±2.5 fps across multiple scenes, confirming runtime stability. The algorithm was tested on both indoor and outdoor VR scenes, with additional robustness tests under dynamic object and gaze shifts. The mathematical modeling of the sensitivity score and adaptive resolution mapping is lightweight, enabling real-time execution on mid-tier GPUs. This framework supports future deployment in both high-fidelity immersive experiences and resource-constrained VR systems.
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DOI: https://doi.org/10.31449/inf.v49i23.9575
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