3D reconstruction and landscape restoration of garden landscapes: an innovative approach combining deep features and graph structures - Chen, Jialin; Cui, Qingwei; Ye, Yu (Frontiers in Environmental Science, 2025)
With the continuous development of landscape restoration technology, how to use modern technology to efficiently reconstruct degraded and damaged historical gardens to help them restore and protect has become an important topic. Traditional 3D reconstruction methods often face challenges in accuracy and efficiency when facing complex garden geometry and ecological environment. To this end, this paper proposes a hybrid model DGA-Net that combines deep convolutional network (DCN), graph convolutional network (GCN) and attention mechanism to improve the 3D reconstruction accuracy and detail recovery in historical garden landscape restoration. DGA-Net extracts spatial features through DCN, uses GCN to model the topological relationship of point clouds, and optimizes the recovery of key geometric details by combining attention mechanism. Compared with traditional methods, this hybrid method shows better performance in the reconstruction of complex structures and ecological characteristics of historical gardens, especially in the accuracy of point cloud generation and detail recovery. Experimental results show that DGA-Net can reconstruct the structure and ecological characteristics of historical gardens more finely, providing higher reconstruction accuracy and efficiency. This study provides innovative technical support for digital modeling and monitoring in landscape restoration, especially in the fields of ecological environment restoration and cultural heritage protection.