MODELING THE PROCESS OF LOADING 3D MODELS IN A CLIENT APPLICATION
Article Sidebar
Main Article Content
Abstract
Relevance. The growing use of client-side AR applications increases the demands on the speed of loading and rendering 3D models. Network conditions and the resource constraints of client devices make it necessary to develop simple yet adequate mathematical models of the loading process. Such models enable the identification of bottlenecks and the development of effective optimization methods aimed at reducing waiting time and improving the user experience. Object of the study: the process of loading and rendering 3D models in a client application with augmented reality elements. Purpose of the article: to develop a simplified mathematical model of the 3D model loading process in the client-side web application implemented with Angular, which takes into account key parameters – the size of the model data, network bandwidth, and processing delays – and serves as a foundation for the development of methods to optimize loading time. Research results. The study presents a mathematical model that describes the total time T required for loading and rendering 3D models in a client-side AR application. The model formalizes the relationships between the model data size, network bandwidth, processing delays, the overhead constant t0, and the optimization coefficient δ. The derived analytical dependencies make it possible to evaluate the influence of each parameter on the total time T and to use them as a basis for methods of optimizing the transmission and preprocessing of 3D content. The model has been verified, which confirmed its adequacy within the defined assumptions and parameter ranges. Conclusions. A simplified model is proposed that demonstrates both flexibility and ease of application: through the introduction of the overhead constant and the optimization coefficient δ, it can be adapted to different scenarios of client application operation and varying network conditions. The derived analytical dependencies may be applied in the development of practical optimization methods aimed at reducing the loading time of 3D models and improving the user experience in AR clients. Scope of application of the obtained results: client-side AR applications (mobile and web-based), systems for preprocessing and delivery of 3D content, and optimization tools for frontend development projects (including those implemented with Angular).
Article Details
References
Sosna, T, Vochozka, V, Šerý, M and Blažek, J (2025), “Developing pupils’ creativity through 3D modeling: an experimental study”, Front. Educ., vol. 10, article number 1583877, doi: https://doi.org/10.3389/feduc.2025.1583877
Fernández-Moyano, J. A., Remolar, I. and Gómez-Cambronero, Á. (2025), “Reality’s Impact in Industry – A Scoping Review”, Applied Sciences, vol. 15(5), article number 2415, doi: https://doi.org/10.3390/app15052415
Kuchuk, H. and Kuliahin, A. (2024), “Hybrid Recommender For Virtual Art Compositions With Video Sentiments Analysis”, Advanced Information Systems, vol. 8, no. 1, pp. 70–79, doi: https://doi.org/10.20998/2522-9052.2024.1.09
Kuliahin, A., Kuchuk, H. (2023), “Classified emotion as implicit recommendation system feedback”, 2023 IEEE 4th KhPI Week on Advanced Technology, KhPI Week 2023 – Conf. Proc., doi: https://doi.org/10.1109/KhPIWeek61412.2023.10312976
Halıcı, S.M. and Gül, L.F. (2025), “AI-based augmented reality for architectural mass study”, Virtual Reality, vol. 29, article number 146, doi: https://doi.org/10.1007/s10055-025-01142-z
Carter, E., Sakr, M. and Sadhu, A. (2024), “Augmented Reality-Based Real-Time Visualization for Structural Modal Identification”, Sensors, vol, 24(5), article number 1609, doi: https://doi.org/10.3390/s24051609
Jeon, J. and Woo, W. (2024), “eCAR: edge-assisted Collaborative Augmented Reality Framework”, arXiv, arXiv:2405.06872, doi: https://doi.org/10.48550/arXiv.2405.06872
Li, L., Qiao, X., Lu, Q., Ren, P. and Lin, R. (2020), “Rendering Optimization for Mobile Web 3D Based on Animation Data Separation and On-Demand Loading”, IEEE Access, vol. 8, pp. 88474–88486, doi:
https://doi.org/10.1109/ACCESS.2020.2993613
Kalinin, Y., Kozhushko, A., Rebrov, O., and Zakovorotniy, A. (2022), “Characteristics of Rational Classifications in Game-Theoretic Algorithms of Pattern Recognition for Unmanned Vehicles”, 2022 IEEE 3rd Khpi Week on Advanced Technology Khpi Week 2022 Conference Proceedings, 03-07 October 2022, doi: https://doi.org/10.1109/KhPIWeek57572.2022.9916454
Kuchuk, G., Kharchenko, V., Kovalenko, A. and Ruchkov, E. (2016), “Approaches to selection of combinatorial algorithm for optimization in network traffic control of safety-critical systems”, Proceedings of 2016 IEEE East-West Design and Test Symposium, EWDTS 2016, 7807655, doi: https://doi.org/10.1109/EWDTS.2016.7807655
Filatov, V., Filatova, A., Povoroznyuk, A. and Omarov, S. (2024), “Image classifier for fast search in large databases”, Advanced Information Systems, vol. 8, no. 2, pp. 12–19, doi: https://doi.org/10.20998/2522-9052.2024.2.02
Petrovska, I., Kuchuk, H., Kuchuk, N., Mozhaiev, O., Pochebut, M. and Onishchenko, Yu. (2023), “Sequential Series-Based Prediction Model in Adaptive Cloud Resource Allocation for Data Processing and Security”, 2023 13th International Conference on Dependable Systems, Services and Technologies, DESSERT 2023, 13–15 October, Athens, Greece, code 197136, doi: https://doi.org/10.1109/DESSERT61349.2023.10416496
Boutsi, A.-M., Ioannidis, C. and Verykokou, S. (2023), “Multi-Resolution 3D Rendering for High-Performance Web AR”, Sensors, vol. 23(15), article number 6885, doi: https://doi.org/10.3390/s23156885
Abdallah, M., Sawalhi, G., Mazhar, A., AlRifaee, M. and Salah, M. (2024), “Factors Influencing the Quality of Augmented Reality Applications”, Procedia Computer Science, vol. 251, pp. 150–156, doi: https://doi.org/10.1016/j.procs.2024.11.095
Ibrаhimov, B., Hashimov, E. and Ismayılov, T. (2024), “Research and analysis mathematical model of the demodulator for assessing the indicators noise immunity telecommunication systems”, Advanced Information Systems, vol. 8, no. 4, pp. 20–25, doi: https://doi.org/10.20998/2522-9052.2024.4.03
Zakovorotniy, A., and Kharchenko, A. (2021), “Optimal speed controller design with interval type-2 fuzzy sets”, 2021 IEEE 2nd KhpiWeek on Advanced Technology, pp. 363–366, doi: https://doi.org/10.1109/KhPIWeek53812.2021.9570045
Kuchuk, H., Mozhaiev, O., Kuchuk, N., Tiulieniev, S., Mozhaiev, M., Gnusov, Y., Tsuranov, M., Bykova, T., Klivets, S. and Kuleshov, A. (2024), “Devising a method for the virtual clustering of the Internet of Things edge environment”, Eastern-European Journal of Enterprise Technologies, vol. 1, no. 9 (127), pp. 60–71, doi: https://doi.org/10.15587/1729-4061.2024.298431
Pandey, A. and Srivastava T. (2021), “Mathematical Modelling: Growing Role and Applications”, Journal of Applied Science and Education (JASE), vol. 1, pp. 1–11, doi: https://doi.org/10.54060/JASE/001.01.004
Rak, T. (2020), “Modeling Web Client and System Behavior”, Information, vol. 11, no. 6, article number 337, doi: https://doi.org/10.3390/info11060337
Tolosana Calasanz, R., Banares, J. A. and Colom, J.-M. (2018), “Model-driven development of data intensive applications over cloud resources”, Future Generation Computer Systems, vol. 87. pp. 888–909, doi: https://doi.org/10.1016/j.future.2017.12.046
Arronategui, U., Banares, J. A. and Colom, J. M. (2025), “Large scale system design aided by modelling and DES simulation: A Petri net approach”, Software Practice & Experience, vol. 55, no. 2, pp. 243–271, doi: https://doi.org/10.1002/spe.3374
Braga, V. G., Correa, S. L., Cardoso, K. V. and Viana, A. C. (2021), “Data‑Driven Characterization and Modeling of Web Map System Workload”, IEEE Access, vol. 9, pp. 26983–27002, doi: https://doi.org/10.1109/ACCESS.2021.3058622
Kuchuk, N., Kovalenko, A., Kuchuk, H., Levashenko, V. and Zaitseva, E. (2022), “Mathematical Methods of Reliability Analysis of the Network Structures: Securing QoS on Hyperconverged Networks for Traffic Anomalies”, Lecture Notes in Electrical Engineering, vol. 831, pp. 223–241, doi: https://doi.org/10.1007/978-3-030-92435-5_13
Xie, G., Wang, T., Fu, H, Liu, D., Deng, L., Zheng, X., Li, L. and Liao J. (2025), “The role of three-dimensional printing models in medical education: a systematic review and meta-analysis of randomized controlled trials”, BMC Med Educ., vol. 25, article number 826, doi: https://doi.org/10.1186/s12909-025-07187-7
Kuchuk, G.A., Akimova, Yu.A. and Klimenko, L.A. (2000), “Method of optimal allocation of relational tables”, Engineering Simulation, vol. 17(5), pp. 681–689, available at: https://www.scopus.com/record/display.uri?eid=2-s2.0-0034512103&origin=resultslist
Cardoso, L.F.d.S., Kimura, B.Y.L. and Zorzal, E.R. (2024), “Towards augmented and mixed reality on future mobile networks”, Multimed Tools Appl., vol. 83, pp. 9067–9102, doi: https://doi.org/10.1007/s11042-023-15301-4
Kuchuk, H., Husieva, Y., Novoselov, S., Lysytsia, D., Krykhovetskyi, H. (2025), “Load Balancing of the layers Iot Fog-Cloud support network”, Advanced Information Systems, vol. 9, no. 1, pp. 91–98, doi: https://doi.org/10.20998/2522-9052.2025.1.11
Sahu, D., Nidhi, Prakash, S., Pandey, V. K., Yang. T., Rathore, R. S. and Wang, L. (2025), “Edge assisted energy optimization for mobile AR applications for enhanced battery life and performance”, Scientific Reports, vol. 15, article number 10034, doi: https://doi.org/10.1038/s41598-025-93731-w
Zhang, H., Li, L., Lu, Q., Yue, Y., Huang, Y. and Dustdar, S. (2024), “Distributed realtime rendering in decentralized network for mobile web augmented reality”, Future Generation Computer Systems, vol. 158, pp. 530–544, doi: https://doi.org/10.1016/j.future.2024.04.050