CROWD-SOURCED SURVEYING FOR BUILDING ARCHAEOLOGY: THE POTENTIAL OF STRUCTURE FROM MOTION (SFM) AND NEURAL RADIANCE FIELDS (NERF)

7 citations as recorded by crossref.

1. Machine learning in architectural heritage: a review of research trends and applications S. Liu et al. https://doi.org/10.1080/13467581.2026.2725985
2. Comparative Assessment of Neural Radiance Fields and Photogrammetry in Digital Heritage: Impact of Varying Image Conditions on 3D Reconstruction V. Croce et al. https://doi.org/10.3390/rs16020301
3. Artificial Intelligence for Digital Heritage Innovation: Setting up a R&D Agenda for Europe S. Münster et al. https://doi.org/10.3390/heritage7020038
4. Accessible 3D Gaussian Splatting from Consumer-Grade UAVs as a Framework for Rapid Documentation of Cultural Heritage A. Martino & F. Chiabrando https://doi.org/10.3390/heritage9080328
5. AI-Based 3D Modeling Strategies for Civil Infrastructure: Quantitative Assessment of NeRF and Photogrammetry E. Atencio et al. https://doi.org/10.3390/s26030852
6. Efficient 3D reconstruction of heritage monuments using symmetry-aware part-based modeling B. Sharma & S. Jana https://doi.org/10.1007/s00371-026-04669-7
7. Assessment of 3D Model for Photogrammetric Purposes Using AI Tools Based on NeRF Algorithm M. Pepe et al. https://doi.org/10.3390/heritage6080301