PERFORMANCE EVALUATION OF SCENE LOADING OPTIMIZATION IN A WEBAR APPLICATION
DOI:
https://doi.org/10.26906/SUNZ.2026.2.146Keywords:
web, augmented reality, webAr, performance evaluation, optimization, Draco, AngularAbstract
Relevance. WebAR is rapidly evolving, however, it faces the challenge of high computational loads on devices. The synchronous loading of heavy 3D models during scene initialization often leads to delays and blocks the browser's main execution thread. Applying comprehensive optimization methods (such as transitioning to the GLB format and utilizing Draco geometry compression) is crucial for ensuring stability, yet their implementation requires an objective quantitative evaluation. The aim of this study is to evaluate the performance of WebAR application scene loading optimization during the initialization stage. The object of the research is the initialization and 3D content loading process within a client WebAR application running on a laptop. The subject of the research encompasses the performance metrics of the WebAR application's optimization methods. Conclusion. Based on the results of instrumental profiling, the high efficiency of transitioning to the GLB format and employing Draco compression has been confirmed. A 47.7% reduction in the peak allocated JS Heap memory was recorded. The overall scene initialization time was reduced by 30.5%, dropping from 4749 to 3298 ms. Furthermore, the main thread idle time was significantly decreased by 45%, and the parsing phase duration by 66.2%, which successfully eliminated critical bottlenecks in the 3D asset preparation pipeline.Downloads
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