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.
Machine Learning and Deep Learning for the Built Heritage Analysis: Laser Scanning and UAV-Based Surveying Applications on a Complex Spatial Grid Structure
The reconstruction of 3D geometries starting from reality-based data is challenging and timeconsuming due to the difficulties involved in modeling existing structures and the complex nature of built heritage. This paper presents a methodological approach for the automated segmentation and classification of surveying outputs to improve the interpretation and building information modeling from laser scanning and photogrammetric data. The research focused on the surveying of reticular, space grid structures of the late 19th–20th–21st centuries, as part of our architectural heritage, which might require monitoring maintenance activities, and relied on artificial intelligence (machine learning and deep learning) for: (i) the classification of 3D architectural components at multiple levels of detail and (ii) automated masking in standard photogrammetric processing. Focusing on the case study of the grid structure in steel named La Vela in Bologna, the work raises many critical issues in space grid structures in terms of data accuracy, geometric and spatial complexity, semantic classification, and component recognition.