NERF FOR HERITAGE 3D RECONSTRUCTION
Conventional or learning-based 3D reconstruction methods from images have clearly shown their potential for 3D heritage documentation. Nevertheless, Neural Radiance Field (NeRF) approaches are recently revolutionising the way a scene can be rendered or reconstructed in 3D from a set of oriented imag...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-06-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-archives.copernicus.org/articles/XLVIII-M-2-2023/1051/2023/isprs-archives-XLVIII-M-2-2023-1051-2023.pdf |
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| author | G. Mazzacca G. Mazzacca A. Karami S. Rigon E. M. Farella P. Trybala F. Remondino |
| author_facet | G. Mazzacca G. Mazzacca A. Karami S. Rigon E. M. Farella P. Trybala F. Remondino |
| author_sort | G. Mazzacca |
| collection | DOAJ |
| description | Conventional or learning-based 3D reconstruction methods from images have clearly shown their potential for 3D heritage documentation. Nevertheless, Neural Radiance Field (NeRF) approaches are recently revolutionising the way a scene can be rendered or reconstructed in 3D from a set of oriented images. Therefore the paper wants to review some of the last NeRF methods applied to various cultural heritage datasets collected with smartphone videos, touristic approaches or reflex cameras. Firstly several NeRF methods are evaluated. It turned out that Instant-NGP and Nerfacto methods achieved the best outcomes, outperforming all other methods significantly. Successively qualitative and quantitative analyses are performed on various datasets, revealing the good performances of NeRF methods, in particular for areas with uniform texture or shining surfaces, as well as for small datasets of lost artefacts. This is for sure opening new frontiers for 3D documentation, visualization and communication purposes of digital heritage. |
| first_indexed | 2024-03-13T03:13:23Z |
| format | Article |
| id | doaj.art-d1a94bbe7b55402e94a03021eb6cd4f9 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-03-13T03:13:23Z |
| publishDate | 2023-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-d1a94bbe7b55402e94a03021eb6cd4f92023-06-26T10:19:08ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342023-06-01XLVIII-M-2-20231051105810.5194/isprs-archives-XLVIII-M-2-2023-1051-2023NERF FOR HERITAGE 3D RECONSTRUCTIONG. Mazzacca0G. Mazzacca1A. Karami2S. Rigon3E. M. Farella4P. Trybala5F. Remondino63D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Trento, ItalyDept. Mathematics, Computer Science and Physics, University of Udine, Italy3D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Trento, Italy3D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Trento, Italy3D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Trento, Italy3D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Trento, Italy3D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Trento, ItalyConventional or learning-based 3D reconstruction methods from images have clearly shown their potential for 3D heritage documentation. Nevertheless, Neural Radiance Field (NeRF) approaches are recently revolutionising the way a scene can be rendered or reconstructed in 3D from a set of oriented images. Therefore the paper wants to review some of the last NeRF methods applied to various cultural heritage datasets collected with smartphone videos, touristic approaches or reflex cameras. Firstly several NeRF methods are evaluated. It turned out that Instant-NGP and Nerfacto methods achieved the best outcomes, outperforming all other methods significantly. Successively qualitative and quantitative analyses are performed on various datasets, revealing the good performances of NeRF methods, in particular for areas with uniform texture or shining surfaces, as well as for small datasets of lost artefacts. This is for sure opening new frontiers for 3D documentation, visualization and communication purposes of digital heritage.https://isprs-archives.copernicus.org/articles/XLVIII-M-2-2023/1051/2023/isprs-archives-XLVIII-M-2-2023-1051-2023.pdf |
| spellingShingle | G. Mazzacca G. Mazzacca A. Karami S. Rigon E. M. Farella P. Trybala F. Remondino NERF FOR HERITAGE 3D RECONSTRUCTION The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | NERF FOR HERITAGE 3D RECONSTRUCTION |
| title_full | NERF FOR HERITAGE 3D RECONSTRUCTION |
| title_fullStr | NERF FOR HERITAGE 3D RECONSTRUCTION |
| title_full_unstemmed | NERF FOR HERITAGE 3D RECONSTRUCTION |
| title_short | NERF FOR HERITAGE 3D RECONSTRUCTION |
| title_sort | nerf for heritage 3d reconstruction |
| url | https://isprs-archives.copernicus.org/articles/XLVIII-M-2-2023/1051/2023/isprs-archives-XLVIII-M-2-2023-1051-2023.pdf |
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