SAM-Based Detection of Structural Anomalies in 3D Models for Preserving Cultural Heritage
Published in 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2024), 2024
Abstract
The detection of structural defects and anomalies in cultural heritage emerges as an essential component to ensure the integrity and safety of buildings, plan preservation strategies, and promote the sustainability and durability of buildings over time. In the search to enhance the effectiveness and efficiency of structural health monitoring of cultural heritage, this work aims to develop an automated method focused on detecting unwanted materials and geometric anomalies on the 3D surfaces of ancient buildings. In this study, the proposed solution combines an AI-based technique for fast-forward image labeling and a fully automatic detection of target classes in 3D point clouds. As an advantage of our method, the use of spatial and geometric features in the 3D models enables the recognition of target materials in the whole point cloud from seed, resulting from partial detection in a few images. The results demonstrate the feasibility and utility of detecting self-healing materials, un wanted vegetation, lichens, and encrusted elements in a real-world scenario.
Recommended citation: Jurado-Rodríguez, D., López, A., Jiménez, J., Garrido, A., Feito, F., & Jurado, J. (2024). SAM-Based Detection of Structural Anomalies in 3D Models for Preserving Cultural Heritage. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (pp. 741–748). 19th International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications. https://doi.org/10.5220/0012464000003660
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