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”.
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.
Connecting geometry and semantics via artificial intelligence: from 3D classification of heritage data to H-BIM representations.
Cultural heritage information systems, such as H-BIM, are becoming more and more widespread today, thanks to their potential to bring together, around a 3D representation, the wealth of knowledge related to a given object of study. However, the reconstruction of such tools starting from 3D architectural surveying is still largely deemed as a lengthy and time-consuming process, with inherent complexities related to managing and interpreting unstructured and unorganized data derived, e.g., from laser scanning or photogrammetry. Tackling this issue and starting from reality-based surveying, the purpose of this paper is to semi-automatically reconstruct parametric representations for H-BIM-related uses, by means of the most recent 3D data classification techniques that exploit Artificial Intelligence (AI). The presented methodology consists of a first semantic segmentation phase, aiming at the automatic recognition through AI of architectural elements of historic buildings within points clouds; a Random Forest classifier is used for the classification task, evaluating each time the performance of the predictive model. At a second stage, visual programming techniques are applied to the reconstruction of a conceptual mock-up of each detected element and to the subsequent propagation of the 3D information to other objects with similar characteristics. The resulting parametric model can be used for heritage preservation and dissemination purposes, as common practices implemented in modern H-BIM documentation systems. The methodology is tailored to representative case studies related to the typology of the medieval cloister and scattered over the Tuscan territory.
The digital anastylosis and the semantic segmentation: the case of the Magna Graecia masks in the Mediterranean area
Fifty years of archaeological activities carried out in the Aeolian Islands have made it possible to bring to light the most complete collection of theatrical masks of the ancient world, an important testimony of the material culture of the theatrical world, during the Classical era. The theatrical masks, preserved at the ‘L. Bernabò Brea’ of Lipari, may be schematized, from a morphological point of view, in three distinct degrees: ‘whole masks’, whole fragments and ‘mute’ fragments. The digital reconstruction and anastylosis workflow follow the same breakdown of these three degrees of status: they have been developed through an inverted pyramid trend, in a scalar and hierarchical way. The universality of the method makes it repeatable and universally applicable to other archaeological finds belonging to a proto-industrial and serial artisan production.
The research aims to define a series of methodologies and techniques to be adopted for the direct survey of archaeological artefacts in fragments and for the definition of a reconstruction and digital anastylosis protocol, with the aim to restore a new memory to the so-called ‘mute’ finds. In order to clarify the genealogical and filiation relationships between the masks, a geometric grid of conspicuous points was identified on each digital model which allowed to rearrange the finds on the basis of their dimensional relationships and to advance, at the same time, a parallel hypothesis of segmentation and semantic annotation, it experimenting the most modern and innovative semantic annotation practices for the Cultural Heritage, in order to improve the understanding, the cataloging and enhancement of the historical data.
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.