@inproceedings{142adfdddedd40bbbcd5270bb68f82ce,
title = "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{\textquoteright}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).",
keywords = "Automatic 3D Reconstruction, Deep Learning, Diffusion Models, Point Clouds",
author = "Pablo Jaramillo and Ivan Sipiran",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; Workshops that were held in conjunction with the 18th European Conference on Computer Vision, ECCV 2024 ; Conference date: 29-09-2024 Through 04-10-2024",
year = "2025",
doi = "10.1007/978-3-031-91572-7\_7",
language = "English",
isbn = "9783031915710",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "104--117",
editor = "\{Del Bue\}, Alessio and Cristian Canton and Jordi Pont-Tuset and Tatiana Tommasi",
booktitle = "Computer Vision – ECCV 2024 Workshops, Proceedings",
}