Cultural Heritage 3D Reconstruction with Diffusion Networks
This work addresses artifact restoration for cultural heritage researchers, but it appears incremental as it applies existing generative AI methods to a new domain.
The paper tackled the problem of repairing cultural heritage objects by using a conditional diffusion model to reconstruct 3D point clouds, achieving accurate reproduction of geometries with considerations for object variability.
This article explores the use of recent generative AI algorithms for repairing cultural heritage objects, leveraging a conditional diffusion model designed to reconstruct 3D point clouds effectively. Our study evaluates the model's performance across general and cultural heritage-specific settings. Results indicate that, with considerations for object variability, the diffusion model can accurately reproduce cultural heritage geometries. Despite encountering challenges like data diversity and outlier sensitivity, the model demonstrates significant potential in artifact restoration research. This work lays groundwork for advancing restoration methodologies for ancient artifacts using AI technologies.