Enhancing Cultural Heritage with Generative AI: A Comparative Framework for the Evaluation of 3D Model Accuracy and Visual Fidelity
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
The digitization of Cultural Heritage (CH) has become a vital tool for preservation and dissemination, with 3D reconstruction playing a key role in capturing intricate geometries and visual details of artifacts. While traditional methods like photogrammetry and laser scanning are effective, they often involve labor-intensive processes and struggle with complex material properties. Recent advancements in Generative AI (GenAI), particularly Large Reconstruction Models (LRMs) such as TRELLIS, offer promising alternatives for 3D generation. However, their application in CH remains underexplored. This paper introduces a novel comparative framework to evaluate the accuracy and visual fidelity of 3D GenAI models in the CH domain. Focusing on TRELLIS, the framework assesses single-view and multi-view 3D generation across five diverse CH scenes, employing both 2D (PSNR, SSIM, LPIPS) and 3D (Chamfer Distance, F-score, Accuracy) metrics. Results demonstrate superior performance for individual artifacts (e.g., Minareto, Greek Vase) compared to complex architectural scenes, with multi-view generation consistently outperforming single-view approaches. The study highlights the potential of GenAI for CH preservation while identifying challenges in large-scale reconstructions, paving the way for future hybrid methodologies and sparse-view optimizations.
Description
CCS Concepts: Computing methodologies → Artificial intelligence; 3D imaging; Computer graphics; Image-based rendering; Information systems → Multimedia content creation
@inproceedings{10.2312:dh.20253146,
booktitle = {Digital Heritage},
editor = {Campana, Stefano and Ferdani, Daniele and Graf, Holger and Guidi, Gabriele and Hegarty, Zackary and Pescarin, Sofia and Remondino, Fabio},
title = {{Enhancing Cultural Heritage with Generative AI: A Comparative Framework for the Evaluation of 3D Model Accuracy and Visual Fidelity}},
author = {Balloni, Emanuele and Paolanti, Marina and Uggeri, Jacopo and Zingaretti, Primo and Pierdicca, Roberto},
year = {2025},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-277-6},
DOI = {10.2312/dh.20253146}
}