3D Point Cloud Text-to-3D Generation for Industrial Scenes via LoRA Fine-tuning and Hypergraph Computing

While 3D point cloud generation has matured for natural scenes, a significant gap remains for industrial applications where geometric precision is paramount. Mainstream methods, trained on general datasets, fundamentally struggle with the structural complexity and regular patterns of manufactured objects, often producing results with severe detail loss and shape ambiguity. In response to these challenges, we introduce a specialized generative architecture designed specifically for industrial scenarios. Our approach establishes a powerful synergy between two key innovations: parameter-efficient LoRA fine-tuning adapts pre-trained models to nuanced industrial semantics, while an advanced graph-based architecture, featuring a hypergraph module, meticulously models high-order structural relationships to ensure precise geometric restoration. Comprehensive experimental results demonstrate that our method significantly outperforms existing approaches in semantic control, structural continuity, and fine-grained detail fidelity. This work provides a robust technical foundation for high-stakes industrial tasks such as high-fidelity digital twin creation, automated defect detection, and predictive structural simulation.