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.

翻译：本文探讨了利用最新的生成式人工智能算法修复文化遗产对象，通过一种旨在有效重建三维点云的条件扩散模型来实现。我们的研究评估了该模型在通用场景和文化遗产特定场景下的性能。结果表明，在考虑对象变异性的前提下，该扩散模型能够精确复现文化遗产的几何形态。尽管面临数据多样性和异常值敏感性等挑战，该模型在文物修复研究中展现出显著潜力。本研究为利用人工智能技术推进古代文物的修复方法学奠定了基础。