Cultural Heritage 3D Reconstruction with Diffusion Networks

Pablo Jaramillo, Ivan Sipiran
10/14/2024
cs.CVcs.AIcs.GR

No official code found. Best implementation: PJaramilloV/pcdiff-method (3 stars, MIT, last commit 2024-09-30).

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.

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Code Implementations(1)

PJaramilloV/pcdiff-methodCommunity100%
MIT

A fork exploring the methodology used for the PyTorch implementation for "Point Cloud Diffusion Models for Automatic Implant Generation" (MICCAI 2023)

31C++, CudaAug 17, 20232 years agoMIT

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Cite this paper

@article{jaramillo2024cultural,
  title  = {Cultural Heritage 3D Reconstruction with Diffusion Networks},
  author = {Pablo Jaramillo and Ivan Sipiran},
  year   = {2024},
  eprint = {2410.10927},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2410.10927v1}
}

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