Adaptive AR Object Streaming Reduces Latency
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Solution Overview
Problem
Conventional augmented reality systems suffer from high latency and inefficiency due to the monolithic download of augmented reality objects, which results in slow start-up times and excessive resource utilization, as they fail to adapt to the client device's position and movement within the scene.
Innovation Solution
The system dynamically identifies and downloads augmented reality objects at varying levels of detail based on real-time features, prioritizing objects within the current and predicted field of view, and utilizing available network resources, thereby reducing start-up latency and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional systems download all augmented reality objects as a monolithic entity before display, then complete scene integrity is maintained, but start-up latency increases significantly
Solution Approach 1:
The patent segments the monolithic augmented reality scene into individual objects or groups of objects that can be downloaded and displayed independently. The system identifies and downloads only the subset of objects visible in the current field of view, allowing the scene to be rendered incrementally rather than requiring complete scene download before display begins.
Solution Approach 2:
The system performs preliminary actions by pre-identifying and prioritizing objects that are most likely to be visible based on the client device's current position and orientation. High-priority objects are downloaded first before lower-priority objects, enabling faster initial rendering while maintaining scene integrity through subsequent downloads.
2Manufacturing precision
If all augmented reality objects are downloaded at full detail, then visual quality is maximized, but memory requirements and network resource utilization increase
Solution Approach 1:
The patent applies local quality by rendering objects at different levels of detail based on their position in the scene and proximity to the client device. Objects within the field of view are rendered at high detail, while objects outside or at a distance are rendered at lower detail or not rendered at all, optimizing memory usage while maintaining visual quality where it matters most.
Solution Approach 2:
The system downloads and renders only the necessary subset of objects required for the current view rather than all objects in the scene. This partial action approach downloads exactly what is needed at the moment, avoiding unnecessary memory consumption from downloading and storing objects that are not currently visible or needed.
3Reliability
If conventional systems download complete augmented reality scenes, then all objects are available for interaction, but network bandwidth consumption increases
Solution Approach 1:
The system performs preliminary analysis of the client device's position, orientation, and movement patterns to predict which objects will be visible and interactable. This preliminary action enables the system to download only those objects that are likely to be needed, maintaining reliability for expected interactions while minimizing network bandwidth consumption.
Solution Approach 2:
The patent implements dynamic object prioritization where the set of downloaded objects changes based on real-time client device movement and viewing conditions. As the device moves through the scene, new objects are identified and downloaded dynamically, ensuring object availability matches actual usage patterns while optimizing network resource utilization.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods that iteratively select versions of augmented reality objects at augmented reality levels of detail to provide for download to a client device to reduce start-up latency associated with providing a requested augmented reality scene. In particular, in one or more embodiments, the disclosed systems determine utility and priority metrics associated with versions of augmented reality objects associated with a requested augmented reality scene. The disclosed systems utilize the determined metrics to select versions of augmented reality objects that are likely to be viewed by the client device and improve the quality of the augmented reality scene as the client device moves through the augmented reality scene. In at least one embodiment, the disclosed systems iteratively select versions of augmented reality objects at various levels of detail until the augmented reality scene is fully downloaded.