Digital Reconstruction of the Jizushan Landscape in Dali Based on Multimodal Large Models

This paper presents a multimodal large model-based approach for the digital reconstruction of Mount Jizushan, a significant Buddhist heritage site in Dali, China. Traditional modeling methods often struggle to capture the semantic richness and structural complexity of such cultural landscapes due to their reliance on unimodal data and manual processes. To overcome these limitations, we propose a framework that integrates images, textual descriptions, and 3D point cloud data using advanced models such as BLIP-2 and LLaVA. The system performs semantic alignment, visual-textual reasoning, and geometry-aware reconstruction, enabling more accurate and meaningful digital representations. Comparative experiments show that our method outperforms conventional photogrammetry in modeling accuracy, visual consistency, and semantic fidelity. Expert reviews and user feedback in virtual environments further validate its effectiveness in immersive cultural heritage applications. Our findings demonstrate the potential of multimodal AI to enhance both the automation and cultural depth of digital landscape reconstruction, offering a scalable solution for heritage preservation and virtual storytelling.