Neural Radiance Fields (Nerf) for Multi-Scale 3d Modeling of Cultural Heritage Artifacts

Abstract

Abstract. This research aims to assess the adaptability of Neural Radiance Fields (NeRF) for the digital documentation of cultural heritage objects of varying size and complexity. We discuss the influence of object size, desired scale of representation, and level of detail on the choice to use NeRF for cultural heritage documentation, providing insights for practitioners in the field. Case studies range from historic pavements to architectural elements or buildings, representing diverse and multi-scale scenarios encountered in heritage documentation procedures. The findings suggest that NeRFs perform well in scenarios with homogeneous textures, variable lighting conditions, reflective surfaces, and fine details. However, they exhibit higher noise and lower texture quality compared to other consolidated image-based techniques as photogrammetry, especially in case of small-scale artifacts.

Automatic Removal of Non-Architectural Elements in 3D Models of Historic Buildings with Language Embedded Radiance Fields

“…Though rival photogrammetric approaches are able to show strong reconstruction performance in many scenarios, they do have multiple drawbacks in their large storage size, lack of novel view synthesis ability, and lack of native methods for manipulation or understanding of the 3D content. To ameliorate these concerns and also to explore the potentialities of more novel methods of 3D reconstruction, we utilized strictly neural radiance field-based approaches following Pepe et al [11], Llull et al [12], and Croce et al [13][14][15] who demonstrate the feasibility of utilizing NeRFs specifically within the cultural heritage domain. In particular, we employed language embedded radiance fields (LERFs) to introduce querying ability to our models and make the identification of extraneous objects possible.…”