This paper aims to investigate the conditioning that the use of playful games requires the role of graphic element for disseminating and promoting the Cultural Heritage. Within the CHROME project, which has, among the various objectives, the definition of an innovative strategy to promote the three Charterhouses of Campania, it comes up with the idea to plan a playful game placed in one of the three monasteries. Its purpose is to provide a first knowledge, both in relation to the spatiality of a Carthusian monastery and to the life of a monk of the Order, exploiting the playful dimension of the games. Since that the proposed location for the game is a monastic complex whose modeling is gained by range-based and image-based survey processes, the project shows the definition of a methodology to generate digital three-dimensional models, whose geometric genesis is at the same time both topologically coherent and enjoyable on the selected technology platform. Once obtained the scene in which the narration develops, it must qualify as a visual device able to activate the sensory involvement, the share and the exploration. For this reason, some expedients (illumination techniques, framing, distortions, sonorous scenes) have been studied to stimulate the player and to communicate cognitive messages related to the game space using the principle “show, don’t tell”.
Re-contextualizing the standing Sekhmet statues in the Temple of Ptah at Karnak through digital reconstruction and VR experience
Recent trends in the Digital Humanities – conceived as new modalities of collaborative, transdisciplinary and computational research and presentation – also strongly influence research approaches and presentation practices in museums. Indeed, ongoing projects in museums have considerably expanded digital access to data and information, documentation and visualization of ancient ruins and objects. In addition, 3D modelling and eXtended Reality opened up new avenues of interacting with a wider public through digital reconstructions that allow both objects and sites to be presented through visual narratives based on multidisciplinary scholarly research. The article illustrates the use of 3D digital reconstruction and virtual reality to re-contextualise standing statues of Sekhmet in the Temple of Ptah at Karnak, where they were found in 1818. Today, they are on display at Museo Egizio, Turin. The theoretical framework of the research and the operational workflow – based on the study of the available archaeological, textual, and pictorial data – is presented here.
Reconstructive 3D Modelling and Interactive Visualization for Accessibility of Piffetti’s Library in the Villa della Regina Museum (Turin)
This research is realised in the framework of a project recently funded as part of the PNRR (National Recovery and Resilience Plan) in the Accessibility sector. The working team has been established in the framework of the scientific agreement between the Museum of Villa della Regina in Turin, the Department of Architecture and Design at Politecnico di Torino, and the Department of History, Drawing and Restoration of Architecture at Sapienza Università di Roma, and includes knowledge from art history, digital surveying, 3D modelling, and digital solutions for cultural heritage. The research involves the reconstructive 3D modelling of Piffetti’s Library, once placed in the cabinet toward midnight and west inside the Villa della Regina and today in the Palazzo del Quirinale, and its interactive visualisation through augmented reality (AR) and virtual reality (VR) aimed at accessibility.
NEURAL RADIANCE FIELDS (NERF) FOR MULTI-SCALE 3D MODELING OF CULTURAL HERITAGE ARTIFACTS
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.
Combining on-site and off-site analysis: towards a new paradigm for cultural heritage surveys
In recent decades, cultural heritage survey practices have significantly evolved due to the increasing use of digitization tools providing quick and easy access to faithful copies of study objects. While these digital data have clear advantages, especially in terms of geometric characterization, they also introduce a paradigm shift by outsourcing ex situ most of the analysis activities. This break between real and virtual working environments now raises new issues, both in terms of data dispersion and knowledge correlation in multidisciplinary teams. Benefiting from the fields of information systems and augmented reality, we proposed an integrated approach allowing the fusion of geometric, visual and semantic features in a single platform. Today, this proof of concept leads to new perspectives for the production of semantically enriched digital data. In this paper, we intend to explore the different possibilities in terms of implementation and their benefits for cultural heritage survey.
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.
A Hierarchical Machine Learning Approach for Multi-Level and Multi-Resolution 3D Point Cloud Classification
The recent years saw an extensive use of 3D point cloud data for heritage documentation, valorisation and visualisation. Although rich in metric quality, these 3D data lack structured information such as semantics and hierarchy between parts. In this context, the introduction of point cloud classification methods can play an essential role for better data usage, model definition, analysis and conservation. The paper aims to extend a machine learning (ML) classification method with a multi-level and multi-resolution (MLMR) approach. The proposed MLMR approach improves the learning process and optimises 3D classification results through a hierarchical concept. The MLMR procedure is tested and evaluated on two large-scale and complex datasets: the Pomposa Abbey (Italy) and the Milan Cathedral (Italy). Classification results show the reliability and replicability of the developed method, allowing the identification of the necessary architectural classes at each geometric resolution.