Comparative Assessment of Neural Radiance Fields and Photogrammetry in Digital Heritage: Impact of Varying Image Conditions on 3D Reconstruction

Abstract

This paper conducts a comparative evaluation between Neural Radiance Fields (NeRF) and photogrammetry for 3D reconstruction in the cultural heritage domain. Focusing on three case studies, of which the Terpsichore statue serves as a pilot case, the research assesses the quality, consistency, and efficiency of both methods. The results indicate that, under conditions of reduced input data or lower resolution, NeRF outperforms photogrammetry in preserving completeness and material description for the same set of input images (with known camera poses). The study recommends NeRF for scenarios requiring extensive area mapping with limited images, particularly in emergency situations. Despite NeRF’s developmental stage compared to photogrammetry, the findings demonstrate higher potential for describing material characteristics and rendering homogeneous textures with enhanced visual fidelity and accuracy; however, NeRF seems more prone to noise effects. The paper advocates for the future integration of NeRF with photogrammetry to address respective limitations, offering more comprehensive representation for cultural heritage preservation tasks. Future developments include extending applications to planar surfaces and exploring NeRF in virtual and augmented reality, as well as studying NeRF evolution in line with emerging trends in semantic segmentation and in-the-wild scene reconstruction.

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

“…The process starts with the acquisition of a series of images, taken from various viewpoints of the same object with sufficient overlap between successive shots. These data undergo dual processing, employing both photogrammetry (via Agisoft Metashape) and NeRF construction (via Nerfstudio), following the method previously described in (Croce et al, 2024). Camera orientation parameters are derived in both cases through alignment on Metashape.…”

“…Remondino et al (2023) proposed various evaluation metrics, including noise level, surface deviation, geometric accuracy, and completeness, to assess NeRF-based reconstruction methods. However, the adaptability and flexibility of use of NeRF to represent cultural objects of varying size and complexity remains an open question (Croce et al, 2024). The issue is crucial in assessing the applicability of NeRF, as its effectiveness could be influenced by size and complexity of the objects being examined: small sculptures, artworks or architectural details, compared to larger monuments as historical buildings or archaeological sites.…”

Evolution of Rendering Based on Radiance Fields. The Palermo Case Study for a Comparison Between Nerf and Gaussian Splatting

“…The advantages of Neural Radiance Field (NeRF) are also being tested in the field of Cultural Heritage. Some research compares and analyses the advantages of using NeRF over the photogrammetric technique or laser scanning, especially in critical conditions such as low number of photos or low image resolution (Croce et al, 2024;Palestini et al, 2021Balloni et al 2023, in the reconstruction of buildings with very difficult conditions from typical ones (Condorelli et al 2021), or also explore the potential of NeRF in the cases of unconventional acquisition conditions (Mazzacca et al 2023).…”

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.…”