Initial assessment on the use of state-of-The-Art nerf neural network 3d reconstruction for heritage documentation


Date

2023

Publication Type

Conference Paper

ETH Bibliography

yes

Citations

Altmetric

Data

Abstract

In recent decades, photogrammetry has re-emerged as a viable solution for heritage documentation. Developments in various computer vision methods have helped photogrammetry to compete against the laser scanning technology, eventually becoming complementary solutions for the purpose of heritage recording. In the last few years, artificial intelligence (AI) has progressively entered various domains including 3D reconstruction. The Neural Radiance Fields (NeRF) method renders a 3D scene from a series of overlapping images, similar to photogrammetry. However, instead of relying on geometrical relations between the image and world spaces, it uses neural networks to recreate the so-called radiance fields. The result is a significantly faster method of recreating 3D scenes. While not designed to generate 3D models, simple computer graphics methods can be used to convert these recreated radiance fields into the familiar point cloud. In this paper, we implemented the Nerfacto architecture to recreate two instances of heritage objects and then compared them to traditional photogrammetric multi-view stereo (MVS). While the initial hypothesis posits that NeRF is not yet capable to reach the level of accuracy and density achieved by MVS as can be observed in the results, NeRF nevertheless shows a great potential due to its fractionally faster processing speed.

Publication status

published

Book title

Volume

XLVIII-M-2-2023

Pages / Article No.

1113 - 1118

Publisher

Copernicus

Event

29th CIPA Symposium “Documenting, Understanding, Preserving Cultural Heritage. Humanities and Digital Technologies for Shaping the Future” (CIPA 2023)

Edition / version

Methods

Software

Geographic location

Date collected

Date created

Subject

NeRF; Neural network; AI; 3D reconstruction; Cultural heritage; Photogrammetry

Organisational unit

09723 - Griess, Verena C. / Griess, Verena C. check_circle

Notes

Funding

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