Artificial Intelligence-Driven 3D Modeling: Transformative Applications Analysis

Kunliang Rao

doi:10.62051/f5wn2q81

Authors

Kunliang Rao

DOI:

https://doi.org/10.62051/f5wn2q81

Keywords:

Artificial Intelligence; 3D Modeling; AI-based Medical; Deep Learning; Industrial Applications.

Abstract

The convergence of Artificial Intelligence (AI) and 3D modeling technologies profoundly changes several key industries. This study analyzes the innovative applications and practical value of AI-powered 3D modeling technology in four major fields: architecture, education, healthcare, and gaming. First, AI technology optimizes design solutions in the construction industry through intelligent algorithms to significantly improve buildings' functionality, energy efficiency, and structural stability. Combined with BIM systems, AI can detect and resolve design conflicts in advance. High-precision 3D digital reconstruction is realized through laser scanning and deep learning for historic building preservation. Meanwhile, 3D modeling technology visualizes abstract knowledge, and virtual anatomical models and historical scene restoration greatly enhance the teaching effect. The AI-driven adaptive learning system analyzes student data and intelligently adjusts the teaching content and rhythm, effectively improving learning efficiency. AI-based medical image 3D reconstruction technology realizes the precise positioning of lesions. Virtual surgery simulation systems and personalized bone substitute designs enhance surgical safety and fitness. The gaming industry utilizes AI to achieve efficient content generation. Deep learning technology makes character behavior more realistic and natural, dramatically improving player immersion. Despite its promising future, the technology still faces data quality, model generalization, and real-time performance challenges. In the future, it is necessary to promote AI and 3D modeling technology from the laboratory to industrial applications by developing cross-industry universal models, optimizing edge computing technology, and improving human-machine collaboration mechanisms.

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References

[1] TOULOUPAKI Eleftheria, and THEODOSIOU Theodoros. Optimization of building form to minimize energy consumption through parametric modelling. Procedia environmental sciences, 2017, 38: 509-514.

[2] PAN Yue, & ZHANG Li Mao. Integrating BIM and AI for smart construction management: Current status and future directions. Archives of Computational Methods in Engineering, 2023, 30(2): 1081-1110.

[3] LI Yuan, et al. 3D LiDAR and multi-technology collaboration for preservation of built heritage in China: A review. International Journal of Applied Earth Observation and Geoinformation, 2023, 116: 103156.

[4] PEAN Thibaul, et al. Review of control strategies for improving the energy flexibility provided by heat pump systems in buildings. Journal of Process Control, 2019, 74: 35-49.

[5] GOKHALE Gargya., CLAESSENS Bert, & DEVELDER Chris. Physics informed neural networks for control oriented thermal modeling of buildings. Applied Energy, 2022, 314: 118852.

[6] TALREJA Sonal. Enhancing anatomical education through artificial intelligence. CyberMedics: Navigating AI and Security in the Medical Field, 2025, 136.

[7] AJUZIEOGU Uchechukwu. Cultural Heritage Reconstruction and Preservation Through Generative AI. Researchgate, 2021, 1-53.

[8] SCOTT Ezequiel, SORIA Alvaro, & CAMPO Marcelo. Adaptive 3D virtual learning environments—A review of the literature. IEEE Transactions on Learning technologies, 2016, 10(3): 262-276.

[9] CINAR Omer Emin, et al. AI-Powered VR for Enhanced Learning Compared to Traditional Methods. Electronics, 2024, 13(23): 4787.

[10] GUNDUZALP Doga, et al. 3d u-netr: Low dose computed tomography reconstruction via deep learning and 3 dimensional convolutions. arXiv preprint arXiv:2105.14130, 2021.

[11] SOOMRO Toufique, et al. Image segmentation for MR brain tumor detection using machine learning: a review. IEEE Reviews in Biomedical Engineering, 2022, 16: 70-90.

[12] CHADDAD Ahmad, WU Yi Hang, & DESROSIERS Christian. Federated learning for healthcare applications. IEEE internet of things journal, 2023, 11(5): 7339-7358.

[13] SUTRADHAR Alok, et al. Designing patient-specific 3D printed craniofacial implants using a novel topology optimization method. Medical & biological engineering & computing, 2016, 54: 1123-1135.

[14] SAFFARI Shaqayeq, DORRIGIV Morteza, & YAGHMAEE Farzin. Harnessing Machine Learning for Procedural Content Generation in Gaming: A Comprehensive Review. Journal of AI and Data Mining, 2024, 12(4): 583-597.

[15] PONTON Jose Luis. Motion Matching for Character Animation and Virtual Reality Avatars in Unity. arXiv preprint arXiv:2310.05215, 2023.

[16] DUNSIN Daniel. The Role of Deep Learning in Enhancing 3D Model Interactions. Researchgate, 2025, 1-4.