id
	13175
Url
	https://chloe.cnr.it/s/BiDiAr/item/13175
Resource template
	Academic Article
Resource class
	bibo:AcademicArticle
Title
	Neural radiance fields (nerf): review and potential applications to digital cultural heritage
Creator
	Croce, Valeria
	Caroti, Gabriella
	De Luca, Livio
	Piemonte, Andrea
	Véron, Philippe
Date
	2023
Language
	eng
Rights
	https://creativecommons.org/licenses/by/4.0/
Abstract
	Abstract. Neural Radiance Fields (NeRF or NeRFs) are to date emerging as a novel method for synthesizing novel views of complex 3D scenes, leveraging an artificial neural network to optimize a volumetric scene function using a set of input views. We conduct a preliminary critical review of the scientific and technical literature on NeRFs, and we highlight possible applications of the latter in the Cultural Heritage domain, for the image-based reconstruction of 3D models of real, multi-scale objects, even in combination with the more well-established photogrammetric techniques. A comparison is made between NeRFs and photogrammetry in terms of operating procedures and outputs (volumetric renderings vs. point clouds or meshes). It is demonstrated that NeRFs could be conveniently used for rendering objects (sculptures, archaeological remains, sites, paintings etc.) that are challenging for photogrammetry, typically: i) metallic, translucent, and/or transparent surfaces; ii) objects that present homogeneous textures; iii) occlusions, vegetation, and elements of very fine detail.
Is Part Of
	The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Doi
	https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-453-2023
Issn
	2194-9034
Pages
	453-460
Short title
	Neural radiance fields (nerf)
Uri
	https://isprs-archives.copernicus.org/articles/XLVIII-M-2-2023/453/2023/
Volume
	XLVIII-M-2-2023
Homepage
	https://www.zotero.org/groups/5293298/bidiar/items/SPXZ8RWH/item-list

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