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Applied Sciences
Article . 2024 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Applied Sciences
Article . 2024
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Application of Neural Radiance Fields (NeRFs) for 3D Model Representation in the Industrial Metaverse

Authors: Lidia Fabra; J. Ernesto Solanes; Adolfo Muñoz; Ana Martí-Testón; Alba Alabau; Luis Gracia;

Application of Neural Radiance Fields (NeRFs) for 3D Model Representation in the Industrial Metaverse

Abstract

This study explores the utilization of Neural Radiance Fields (NeRFs), with a specific focus on the Instant NeRFs technique. The objective is to represent three-dimensional (3D) models within the context of the industrial metaverse, aiming to achieve a high-fidelity reconstruction of objects in virtual environments. NeRFs, renowned for their innovative approach, enable comprehensive model reconstructions by integrating diverse viewpoints and lighting conditions. The study employs tools such as Unity, Photon Pun2, and Oculus Interaction SDK to develop an immersive metaverse. Within this virtual industrial environment, users encounter numerous interactive six-dimensional (6D) models, fostering active engagement and enriching the overall experience. While initial implementations showcase promising results, they also introduce computational complexities. Nevertheless, this integration forms the basis for immersive comprehension and collaborative interactions within the industrial metaverse. The evolving potential of NeRF technology promises even more exciting prospects in the future.

Keywords

Technology, Artificial intelligence, Metaverse, QH301-705.5, COMUNICACION AUDIOVISUAL Y PUBLICIDAD, T, Physics, QC1-999, artificial intelligence, Engineering (General). Civil engineering (General), INGENIERIA DE SISTEMAS Y AUTOMATICA, Chemistry, metaverse, 3D reconstruction, TA1-2040, Biology (General), QD1-999

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
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10
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Average
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205
122
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