journal article·2025-01-15Open access

Optimization of Rendering Parameters of Cesium 3DTiles Model Based on Differential Evolution Algorithm

Doujun Zhang, Yong Wu, Youcong Ni, Tinghuang Zhang, Chenxiang Gao

Applied Sciences· Volume 15· Issue 2

Abstract

Cesium is an open-source 3D virtual earth engine based on WebGL and one of the most widely used 3D GIS engines. The 3DTiles rendering parameters in Cesium improve rendering efficiency but involve numerous data types and complex interdependencies, making simultaneous optimization challenging. In this paper, we proposed a multi-strategy probabilistic discrete differential evolution algorithm (MSPDDE) for finding the optimal values of the rendering parameters of Cesium 3DTiles model, which increases the search space and improves the convergence speed by introducing multiple mutation strategies. These strategies effectively reduce the probability of falling into local optimality due to too many parameters and deal with discrete variables in the rendering parameters by applying a probabilistic discretization strategy to the discrete variables so that MSPDDE is able to find all the rendering parameter optima of Cesium 3DTiles. Under three different sizes of 3DTiles model cases, the rendering model time using the optimal parameter configurations found by the algorithm is reduced by 28.84%, 27.89%, and 13.32%, respectively, compared with the default parameter configurations of Cesium, which shortens the time of rendering 3DTiles models by Cesium.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Image Processing and 3D Reconstruction · Computer Graphics and Visualization Techniques · Robotics and Sensor-Based Localization

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Geographical Sciences, School of Carbon Neutrality Future Technology, Fujian Normal University, Fuzhou 350117, China

Institute of Geography, Fujian Normal University, Fuzhou 350117, China

School of Computer and Network Security, Fujian Normal University, Fuzhou 350117, China

Funding

Dr. Yong Wu

References (35)

Show source-provided references
1. Interdisciplinary urban GIS for smart cities: Advancements and opportunities10.1080/10095020.2013.774108 ↗
2. Cesium (2023, March 10). An Open-Source JavaScript Library for World-Class 3D Globes. Available online: https://cesium.com/.
3. Varlık, A., and Dursun, İ. (2024). Three-Dimensional Web-Based Client Presentation of Integrated BIM and GIS for Smart Cities. Buildings, 14.10.3390/buildings14093021 ↗
4. Web-based agricultural infrastructure digital twin system integrated with GIS and BIM concepts10.1016/j.compag.2023.108441 ↗
5. Liu, Y., Lin, H., Zhao, Z., Bai, W., and Hu, N. (2023). Research on the Visualization of Railway Signal Operation and Maintenance Based on BIM + GIS. Sensors, 23.10.3390/s23135984 ↗
6. Lam, P.D., Gu, B.H., Lam, H.K., Ok, S.Y., and Lee, S.H. (2024). Digital Twin Smart City: Integrating IFC and CityGML with Semantic Graph for Advanced 3D City Model Visualization. Sensors, 24.10.3390/s24123761 ↗
7. Buyukdemircioglu, M., and Kocaman, S. (2020). Reconstruction and Efficient Visualization of Heterogeneous 3D City Models. Remote Sens., 12.10.3390/rs12132128 ↗
8. Pang, Y., Jia, Q., Li, J., Yang, A., Yang, Q., Shang, X., and Zuo, C. (2023, January 6–8). Optimizing loading strategy for visualizing 3D photo-realistic model using human eye’s area of interest. Proceedings of the International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2023), Changsha, China.10.1117/12.2682649 ↗
9. Cross-platform online visualization system for open BIM based on WebGL10.1007/s11042-018-5820-0 ↗
10. Zhan, W., Chen, Y., and Chen, J. (2021). 3D Tiles-Based High-Efficiency Visualization Method for Complex BIM Models on the Web. ISPRS Int. J. Geo-Inf., 10.10.3390/ijgi10070476 ↗
11. Approaches Improving Cesium Rendering Performance for Displaying Massive 3D Models
12. Study on the loading optimization strategy of tilt photography based on Cesium
13. Loading Optimization Strategy of 3D Scene Data in Cesium
14. Song, Z., and Li, J. (2018, January 27–29). A dynamic tiles loading and scheduling strategy for massive oblique photogrammetry models. Proceedings of the 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), Chongqing, China.10.1109/ICIVC.2018.8492731 ↗
15. Cesium (2023, March 17). 3DTiles Render Parameters. Available online: https://cesium.com/learn/cesiumjs/ref-doc/Cesium3DTileset.html.
16. Jiang, C., and Xugang, L. (2024). Pnts tile algorithm for full attribute conversion of LiDAR point cloud to Cesium. Bull. Surv. Mapp., 135–139.
17. Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces10.1023/A:1008202821328 ↗
18. Differential Evolution Mutations: Taxonomy, Comparison and Convergence Analysis10.1109/ACCESS.2021.3077242 ↗
19. On convergence of differential evolution over a class of continuous functions with unique global optimum10.1109/TSMCB.2011.2160625 ↗
20. Influence of crossover on the behavior of differential evolution algorithms10.1016/j.asoc.2009.02.012 ↗
21. Differential Evolution: A Survey of the State-of-the-Art10.1109/TEVC.2010.2059031 ↗
22. An Overview of Self-Adaptive Differential Evolution Algorithms with Mutation Strategy10.18280/mmep.090419 ↗
23. Mendes, R., and Mohais, A.S. (2005, January 2–5). DynDE: A differential evolution for dynamic optimization problems. Proceedings of the 2005 IEEE Congress on Evolutionary Computation, Edinburgh, UK.
24. Eltaeib, T., and Mahmood, A. (2018). Differential Evolution: A Survey and Analysis. Appl. Sci., 8.10.3390/app8101945 ↗
25. Yaman, A., Iacca, G., and Caraffini, F. (2019, January 4–8). A comparison of three differential evolution strategies in terms of early convergence with different population sizes. Proceedings of the AIP Conference Proceedings, Bodrum, Turkey.10.1063/1.5089969 ↗
26. Pang, T., Wei, J., Chen, K., Wang, Z., and Sheng, W. (2022, January 18–23). An adaptive differential evolution with mutation strategy pools for global optimization. Proceedings of the 2022 IEEE Congress on Evolutionary Computation (CEC), Padua, Italy.10.1109/CEC55065.2022.9870292 ↗
27. Differential evolution with multi-population based ensemble of mutation strategies10.1016/j.ins.2015.09.009 ↗
28. Energy optimisation for the combination of turning and drilling features using differential evolution
29. Multi-strategy co-evolutionary differential evolution for mixed-variable optimization10.1016/j.knosys.2021.107366 ↗
30. Services oriented smart city platform based on 3D city model visualization10.5194/isprsannals-II-4-59-2014 ↗
31. Statistical analysis of the application of Wilcoxon and Mann-Whitney U test in medical research studies
32. Comparative Power of Student t-Test and Mann-Whitney U Test for Unequal Sample Sizes and Variances10.1080/00220973.1987.10806451 ↗
33. Lampinen, J., and Zelinka, I. (1999, January 12–15). Mixed integer-discrete-continuous optimization by differential evolution. Proceedings of the 5th International Conference on Soft Computing, Oxford, UK.
34. Machine learning algorithms—A review
35. Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping10.1016/j.jenvman.2018.03.089 ↗