An SfM system for mural digitization with attention-guided feature matching and robust sparse reconstruction

Murals hold profound historical and artistic value, but their inevitable deterioration makes mural cultural heritage digitization increasingly urgent. Traditional Structure-from-Motion (SfM) methods often fail on mural data due to repetitive textures, low-texture regions, and the absence of camera metadata, resulting in poor feature matching and unstable reconstruction. To address these issues, this paper proposes a mural-oriented SfM system that integrates an attention-guided feature matching algorithm with a customized sparse reconstruction pipeline, including focal length estimation and edge-based bundle adjustment. Experiments conducted on mural datasets from the Mogao Grottoes demonstrate that the proposed system significantly improves reconstruction accuracy and robustness, providing a reliable technical foundation for large-scale mural digitization and offering a practical solution for preserving and studying mural heritage.