1. Koch J., Gomse M., Schüppstuhl T. Digital game-based examination for sensor placement in context of an Industry 4.0 lecture using the Unity 3D engine – a case study. Procedia Manufacturing, 2021, Vol. 55, Pp. 563–570. DOI: 10.1016/j.promfg.2021.10.077
2. Anikeev A.S. Generating and modeling 2D terrain from control values using Unity in the C# programming language. Engineering Journal of Don, 2020, Vol. 64, No. 4, Art. no. 16.
3. Krajčovič M., Gabajová G., Matys M., Grznár P., Dulina Ľ., Kohár R. 3D Interactive Learning Environment as a Tool for Knowledge Transfer and Retention. Sustainability, 2021, Vol. 13, No. 14, Art. no. 7916. DOI: 10.3390/su13147916
4. Helbig C., Becker A.M., Masson T., Mohamdeen A., Sen Ö.O., Schlink U. A game engine based application for visualising and analysing environmental spatiotemporal mobile sensor data in an urban context. Frontiers in Environmental Science, 2022, Vol. 10, Art. no. 952725. DOI: 10.3389/fenvs.2022.952725
5. Wang Z., Wu G., Boriboonsomsin K., Barth M.J., Han K., Kim B., Tiwari P. Cooperative Ramp Merging System: Agent-Based Modeling and Simulation Using Game Engine. SAE International Journal of Con-nected and Automated Vehicles, 2019, Vol. 2, no. 2, pp. 115–126. DOI: 10.4271/12-02-02-0008
6. Geris A., Cukurbasi B., Kilinc M., Teke O. Balancing performance and comfort in virtual reality: A study of FPS, latency, and batch values. Software: Practice and Experience, 2024, Vol. 54, No. 12, Pp. 2336–2348. DOI: 10.1002/spe.3356
7. Gonakhchian V.I. Performance model of graphics pipeline for single-pass dynamic 3D scene rendering scheme. Proceedings of the Institute for System Programming of the RAS, 2020, Vol. 32, No. 4, Pp. 53–72. DOI: 10.15514/ISPRAS-2020-32(4)-4
8. Han S., Sander P.V. Triangle reordering for efficient rendering in complex scenes. Journal of Compu-ter Graphics Techniques, 2017, Vol. 6, No. 3, Pp. 38–52.
9. Hasselgren J., Andersson M., Akenine-Möller T. Masked software occlusion culling. Proceedings of High Performance Graphics (HPG’16), 2016, pp. 23–31. DOI: 10.2312/hpg.20161189
10. Jiang T., Huang S., Yan Z. Rendering Optimization of Building Information Models Based on Occlusion Culling. Proceedings of the 2024 4th International Conference on Public Management and Big Data Analysis (PMBDA 2024), 2025, Pp. 358–368. DOI: 10.2991/978-94-6463-656-7_35
11. Pantazopoulos I., Tzafestas S. Occlusion culling algorithms: A comprehensive survey. Journal of Intel-ligent and Robotic Systems, 2002, Vol. 35, Pp. 123–156. DOI: 10.1023/A:1021175220384
12. Ye L., Liu G., Chen G., Li K., Chen Q., Fan W., Zhang J. 3D Model Occlusion Culling Optimization Method Based on WebGPU Computing Pipeline. Computer Systems Science and Engineering, 2023, Vol. 47, No. 2, Pp. 2529–2545. DOI: 10.32604/csse.2023.041488
13. Lee E.-S., Shin B.-S. Vertex Chunk-Based Object Culling Method for Real-Time Rendering in Metaverse. Electronics, 2023, Vol. 12, No. 12, Art. no. 2601. DOI: 10.3390/electronics12122601
14. Ding Y., Song Y. Vision-Degree-Driven Loading Strategy for Real-Time Large-Scale Scene Rende-ring. Computers, 2025, Vol. 14, No. 7, Art. no. 260. DOI: 10.3390/computers14070260
15. Kiusya Z., Gikunda P., Musumba G. Adaptive mesh compression algorithm for near real-time rendering of large-scale static scenes. 2025 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA), 2025, Pp. 1–8. DOI: 10.1109/ACDSA65407.2025.11166552
16. Rossi A., Barbiero M., Scremin P., Carli R. Robust Visibility Surface Determination in Object Space via Plücker Coordinates. Journal of Imaging, 2021, Vol. 7, No. 6, Art. no. 96. DOI: 10.3390/jimaging7060096
17. Sun Y., Huang Q., Hsiao D.-Y., Guan L., Hua G. Learning view selection for 3D scenes. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, Pp. 14459–14468. DOI: 10.1109/CVPR46437.2021.01423
18. Nousias S., Arvanitis G., Lalos A., Moustakas K. Deep saliency mapping for 3D meshes and applications. ACM Transactions on Multimedia Computing, Communications and Applications, 2023, Vol. 19, No. 2, Art. no. 71. DOI: 10.1145/3550073
19. Nousias S., Arvanitis G., Lalos A., Moustakas K. Mesh saliency detection using convolutional neural networks. 2020 IEEE International Conference on Multimedia and Expo (ICME), 2020, Pp. 1–6. DOI: 10.1109/ICME46284.2020.9102796
20. Sarem M., Zheng Y., Wang K., Albshlawy L. An Improved Deep Learning Based RGB-D Saliency Detection Model. 2025 IEEE 18th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC), 2025, Pp. 105–112. DOI: 10.1109/MCSoC67473.2025.00027
21. Ullah I., Jian M., Hussain S., Guo J., Yu H., Wang X., Yin Y. A brief survey of visual saliency detection. Multimedia Tools and Applications, 2020, Vol. 79, pp. 34605–34645. DOI: 10.1007/s11042-020-08849-y
22. Lee C.H., Varshney A., Jacobs D.W. Mesh saliency. SIGGRAPH’05: ACM SIGGRAPH 2005 Papers, 2005, Pp. 659–666. DOI: 10.1145/1186822.1073244
23. Vázquez P.P., Feixas M., Sbert M., Llobet A. Viewpoint entropy: a new tool for obtaining good views of molecules. Joint EUROGRAPHICS – IEEE TCVG Symposium on Visualization, 2002, Pp. 1–6.
24. Genova K., Savva M., Chang A.X., Funkhouser T. Learning where to look: Data-driven viewpoint set selection for 3D scenes. arXiv:1704.02393, 2017. DOI: 10.48550/arXiv.1704.02393
25. Patney A., Salvi M., Kim J., Kaplanyan A., Wyman C., Benty N., Luebke D., Lefohn A. Towards foveated rendering for gaze-tracked virtual reality. ACM Transactions on Graphics (TOG), 2016, Vol. 35, No. 6, Art. no. 179. DOI: 10.1145/2980179.2980246
26. Chernyi V.G., Bolsunovskaya M.V. Automation of object selection for rendering optimization in Unity. Razvitie intellektual'noi ekonomiki i promyshlennosti na osnove iskusstvennogo intellekta [Development of intel-lectual economy and industry based on artificial intelligence], 2025, Pp. 755–771. DOI: 10.18720/IEP/2025.3/36
27. Meister D., Ogaki S., Benthin C., Doyle M.J., Guthe M., Bittner J. A Survey on Bounding Volume Hierarchies for Ray Tracing. Computer Graphics Forum, 2021, Vol. 40, No. 2, Pp. 683–712. DOI: 10.1111/cgf.142662
28. Meister D., Bittner J. Performance Comparison of Bounding Volume Hierarchies for GPU Ray Tra-cing. Journal of Computer Graphics Techniques, 2022, Vol. 11, No. 3, pp. 1–19.