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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. [ABSTRACT FROM AUTHOR]

The reconstruction of 3D geometries starting from reality-based data is challenging and time-consuming 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. [ABSTRACT FROM AUTHOR]

Terrestrial laser scanners (TLS) are used for a variety of applications, e.g., surveying, forestry, cultural heritage preservation, mining, topographic mapping, urban planning, forensics etc. This technology has made a huge shift in 3D spatial data collection due to much faster speed compared to other techniques. In the absence of guiding principles for positioning TLS relative to an object, surveyors collect data at maximum arrangements of scanning geometry elements due to fear of incomplete data of TLS. In 3D spatial data acquisition, positional accuracy and Level of Detail (LOD) are major considerations and are dependent on laser incident angle, footprint size, range and resolution. Mathematical models have been developed relating range, incident angle and laser footprint size for different surface configurations. These models can be used to position TLS to collect data at required positional accuracy and LOD. Models have been verified by deriving one model from other surface models by changing parameters. Effects of incident angle and footprint size have been studied mathematically and experimentally on a natural sloping surface. From the results, surveyors can plan the positioning of the scanner so that data is collected at the required accuracy and LOD. [ABSTRACT FROM AUTHOR]

In recent years many activities were conducted to commemorate the 100th Anniversary of the First World War (WWI) outbreak. Among these, the valorisation of history and landscape (VAST) project ( http://vast.fbk.eu ) was part of the initiatives promoted by the Autonomous Province of Trento (Italy) as a tribute to WWI events in the region. The project was primarily aimed to document and promote, through 3D digitization approaches, ICT technologies and communication material, the memory of sites, theatre of the world conflict. The Trento's area was under the Austro-Hungarian Empire until the end of WWI and on the border with the Italian Kingdom. The area represented a crucial and bloody war front between the Austrian and Italian territories. It was thus constellated of military fortresses, trenches and tunnels, most of them now ruined and at risk to slowly disappear. 3D surveying and modelling techniques were exploited to produce 3D digital models of structures and objects, along with virtual tours, dissemination material and a WebGIS of the area. All the products are now used for restoration, valorisation, educational and communication purposes. [ABSTRACT FROM AUTHOR]

The integration of Terrestrial Laser Scanning (TLS) and Structure from Motion and MultiView Stereo techniques allows to obtain comprehensive models of complex objects by using each technique in contexts presenting the optimal operating conditions, as widely reported in bibliographic references. A different situation occurs for emergency surveys. In this case, time and security act as constraining factors, requiring the use of these techniques also in the most unfavourable conditions. In the case of photogrammetry, these include areas where the object surfaces are not perpendicular to the camera axis, and in the case of TLS, they include areas where laser beams are almost tangent to the surveyed object surfaces. These situations are anyway necessary for safely carrying out these surveys in the minimum possible time and cost. Although this kind of survey results locally in lower precision levels than those obtainable by these techniques in ideal conditions, it entails the possibility of obtaining complete models, e.g. including vertical external walls in inaccessible buildings, with controlled precision. [ABSTRACT FROM AUTHOR]

Mobile and handheld mapping systems are becoming widely used nowadays as fast and cost-effective data acquisition systems for 3D reconstruction purposes. While most of the research and commercial systems are based on active sensors, solutions employing only cameras and photogrammetry are attracting more and more interest due to their significantly minor costs, size and power consumption. In this work we propose an ARM-based, low-cost and lightweight stereo vision mobile mapping system based on a Visual Simultaneous Localization And Mapping (V-SLAM) algorithm. The prototype system, named GuPho (Guided Photogrammetric System), also integrates an in-house guidance system which enables optimized image acquisitions, robust management of the cameras and feedback on positioning and acquisition speed. The presented results show the effectiveness of the developed prototype in mapping large scenarios, enabling motion blur prevention, robust camera exposure control and achieving accurate 3D results. [ABSTRACT FROM AUTHOR]