3D Gaussian-Driven SAM2 Repair Method]{3D Gaussian-Driven SAM2 Multi-View Fusion Detection and Triple-Constrained Symmetry Plane Generation Repair Method

Advancements in digital conservation have established precise damage localization and automated repair template extraction as critical needs in 3D artifact restoration. Conventional approaches, often limited by heavy manual intervention, high missed-detection rates from single-view analysis, and underutilization of symmetrical features, struggle to meet the demands of high-fidelity restoration. To address these challenges, this paper presents a novel method for 3D artifact damage detection and template extraction, which integrates Segment Anything Model 2 (SAM2) semantic segmentation with geometric symmetry constraints. Our framework begins with high-accuracy 3D reconstruction of the damaged artifact using 3D Gaussian splatting to generate a comprehensive, multi-view image set. SAM2 is then employed to produce damage masks across these views, which are projected into 3D space and integrated using camera parameters. Subsequently, a triple-verification strategy---combining PCA principal axis analysis, minimum symmetry error search, and voxel symmetry evaluation---is proposed to robustly identify the artifact's symmetry plane. This enables the extraction of a structural template from the intact side, which guides the repair of the damaged region. Experimental results demonstrate the efficacy of our method, achieving an average damage detection accuracy of 92.7\%, a symmetry plane detection error below 0.8 mm, and an 89.3\% fit between the extracted template and the damaged area, significantly outperforming traditional single-strategy detection and template extraction techniques. Requiring no large-scale training dataset and reducing reliance on manual input, this approach offers a practical, automated, and high-precision solution for 3D cultural heritage restoration.