Cultural Heritage 3D Reconstruction with Diffusion Networks

Abstract

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 (The dataset is available in: https://github.com/PJaramilloV/Precolombian-Dataset , and the code in https://github.com/PJaramilloV/pcdiff-method ).