Evaluating the performance of a webar application’s scene loading on a mobile device
DOI:
https://doi.org/10.30837/2522-9818.2026.2.052Keywords:
web; augmented reality; WebAR application; performance evaluation; parsing; rendering; AngularAbstract
Subject of the study: the patterns of CPU resource allocation and changes in browser engine timing metrics during the execution of WebAR scene initialization stages. Object of study: the process of initializing and loading 3D content in a client-side WebAR application on a mobile device. The aim of the study is to develop an approach for comprehensive performance evaluation and to identify patterns in the distribution of computational load during the initialization of WebAR applications using deep browser profiling tools. Objectives. Develop a mathematical model of WebAR application operation during the scene initialization phase and decompose the loading process into key stages with an assessment of their time costs. Conduct empirical profiling, identify peak CPU loads and blocking tasks, and formulate well-founded directions for further performance optimization. Methods. Performance was investigated through empirical performance testing of the WebAR application during its most resource-intensive stage – the initialization and loading of a 3D scene. Data collection regarding CPU thread load within the context of a web page was performed using the Chrome DevTools developer tools. A Google Pixel 8 mobile device served as the hardware environment for the experiments, and the statistical reliability of the results was ensured by a 10-fold iteration of each test scenario. Research results. The developed mathematical model formalizes the process of loading a WebAR application scene and allows for the assessment of CPU load at each stage. Based on the analysis of the time profile obtained using the improved mathematical model and Chrome DevTools, four loading stages were identified: initialization of an empty scene, loading a 3D object from the server, model parsing, and model rendering. A nonlinear nature of the CPU load was established. The parsing stage proved to be the most resource-intensive, during which peak CPU load and the longest blocking task were recorded, caused by the synchronous deserialization of binary data. Conclusions. An approach is proposed to improve the performance of loading a WebAR application scene on a mobile device by balancing the CPU load. The study confirmed that the critical phase of initialization is the parsing of the 3D model. Given the simulation results and empirical studies conducted, a promising direction is the implementation of incremental loading and processing of 3D content.Downloads
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