Development of a Digital Twin-Based Visualization System for Restoring Ancient Architectural Sculptures

This study applies digital twin technology to enhance the restoration and monitoring of ancient sculptures, integrating laser point clouds, LoRa-enabled IoT sensors, and GPU rendering for precise, real-time decision support. Using a Ming Dynasty grotto as a case study, the authors propose a dual-loop framework: an acquisition loop with multi-source sensing and normal-constrained semantic segmentation generating a millimeter-level semantic mesh, and a monitoring loop with rule-data hybrid calibration for real-time micro-strain and environmental monitoring. GPU-accelerated pipeline enables interactive visualization, while a state machine ensures twin-physical synchronization. Deployed in 48 hours, the system achieves a stable RMSE of 3.5 mm, 0.3% false negative rate, and an expert score of 4.04/5 (18% higher than controls). This work demonstrates visualized digital twins' value in cultural heritage restoration precision and decision-making, with modular adaptability for cross-type heritage—including agricultural heritage (e.g., ancient terraces, irrigation systems)—preservation.