The contribution examines the case study of the Church of S. Pietro a Coppito in L’Aquila, which underwent significant stylistic restoration in the 1960s, to explore the potential applications and reliability of generative AI in cultural heritage communication. In particular, according to the virtual reconstruction of the ancient configuration, the study investigates the impact of integrating graphical inputs alongside textual prompts for image generation, assessing their effectiveness as a means of guiding AI and improving the quality of the results achieved. Based on graphic, photographic, and historiographical sources, sketches of the interior views of the church were produced. Subsequently, various gen-AI platforms were tested to process these images, evaluating the algorithms’ ability not only to generate coherent and effective visuals but, above all, to understand and interpret the initial graphic inputs. The results show both potential and critical limitations: while AI can produce photorealistic and stylistically coherent renderings, it often introduces arbitrary elements and struggles with strict historical accuracy, especially in architectural and decorative details. The paper highlights the need for deeper human-AI interaction and emphasizes the importance of informed graphical input and critical evaluation in the application of genAI to cultural heritage representation.
A Proposal of Integration of Point Cloud Semantization and VPL for Architectural Heritage Parametric Modeling
The paper proposes a reproducible workflow for the semi-automated parametric modeling of architectural heritage starting from point cloud data acquired through laser scanning and photogrammetry. The methodology integrates automatic semantic segmentation, geometric feature recognition, mathematical analysis in Matlab, and Visual Programming Language (VPL) procedures in Dynamo to support Scan-to-BIM workflows. The system identifies geometric primitives from segmented point clouds, extracts modeling parameters, and generates parametric vault models automatically through a VPL environment. The research aims to reduce subjectivity, modeling time, and manual effort in heritage HBIM processes while improving reproducibility and scalability for architectural documentation and conservation.
Visual Programming for a Machine Semi-Automatic Process of HBIM Models Geometric Evaluation
This paper presents a semi-automatic methodology for evaluating the geometric reliability of HBIM models through the integration of visual programming techniques within BIM environments. The research addresses the challenge of reconciling the parametric standardization of BIM with the geometric complexity and uniqueness of historical architecture, focusing on the need to quantify deviations between survey data and digital models.
The proposed workflow is based on the comparison between point clouds derived from architectural surveys and corresponding HBIM elements, using a Visual Programming Language (VPL) implemented in Dynamo. As illustrated in the operational pipeline (Fig. 3, p. 798), the method involves the import of point cloud data and model geometries, computation of normal vectors, projection of points onto model surfaces, and calculation of distances to evaluate geometric deviation. These values are then classified according to predefined reliability thresholds, generating a Level of Accuracy (LoA) and automatically updating model parameters within the BIM environment. Visualization outputs (Figs. 7–9, pp. 799–801) enable the identification of low, medium, and high reliability zones through color-coded mapping.
Results demonstrate that the proposed approach significantly reduces the time required for reliability assessment and supports iterative model refinement through semi-automated feedback. The study highlights the potential of integrating algorithmic processes within HBIM workflows to improve transparency, reproducibility, and accuracy in heritage modeling, while emphasizing the interpretative nature of digital reconstruction and the importance of explicitly declaring model reliability.
The Sanctuary BVMA in Pescara: AR Fruition of the Pre–Conciliar Layout
The project presented here is addressed to the documentation, the investigation of architectural values and their valorization through an application of Augmented Reality technologies enhanced by an AI based tracking application of the Sanctuary Basilica Madonna dei Sette Dolori (BVMA: Beata Vergine Maria Addolorata) in Pescara. The workflow foresees the use of the numerous images taken for the phases of photogrammetric acquisition of the artefact and images taken from the visualizations of the cloud of laser scanner points in order to carry out the “education” phase of the AI software (so that the program can store the greatest number of images for a self interpretative reconstruction of the geometries). The AI data will then be used as a tracking structure for the AR overlay of the digital model on real space, all through a webXR application usable from any device (HMD, desktop or mobile).
Prosthetic Visualizations for a Smart Heritage
The development of ICT has favoured the spread of real–time, pervasive and ubiquitous applications. In particular, VR and AR visualizations allow a close interrelation between people, data, environments and objects. Consequently, it is possible to enrich cultural heritage with information by visually superimposing multimedia content, in absolute respect of their physical consistency. In this way, it is possible to create a ‘smart heritage’ dimension that combines the potential of the ‘Phygital’ with the protection and enhance-ment of assets often characterized by important elements of fragility. An important role is played by AI applications, which automatically direct the processes of ‘Interpretation’ and ‘Presentation’ of the heritage. Based on the experience of the 3D reconstruction of the no longer existing Baroque configuration of the Basilica of Collemaggio in L’Aquila, aim of the paper is a theoretical–methodological reflection on the concept of VR / AR / MR for cultural heritage.
Visual Programming for a Machine Semi-Automatic Process of HBIM Models Geometric Evaluation
The topic of the relationship between digital restitutive model and measurement can find important development possibilities in machine procedures, in particular in the Historic Building Information Modeling (HBIM) field. In fact, BIM uses parameterized and pre-defined objects in special 3D libraries articulated according to the architectural components, not corresponding to ideal configurations. Moreover, BIM platforms are limited in modeling deformations, damages, and degradations. The paper investigates the advantages of using visual programming to increase the possibilities given by the BIM software, born for new buildings, by carrying out a semi-automatic assessment of the geometric reliability directly in the BIM environment. In particular, the algorithm compares model’s shapes with the cast of the artifact given by the point cloud, and declares it, by automatically filling a dedicated reliability parameter linked to the BIM model element.
A Proposal of Integration of Point Cloud Semantization and VPL for Architectural Heritage Parametric Modeling
Current architectural survey processes utilize point clouds generated by laser scanning and digital photogrammetry. Increasingly, these surveys produce 3D models, particularly parametric models, in what is known as the “scan to 3D model” or “scan to BIM” process. However, the phases of analysis and classification of architectural elements, segmentation and semantization of point clouds, and semi-automatic modeling remain complex and labor-intensive and require an active role commitment of the scholar or modeler. These steps are usually performed manually, resulting in high subjectivity and low reproducibility. This paper proposes a reproducible workflow that automatically segments point clouds, identifies geometric shapes by comparing them with a library of ideal geometries, and extracts necessary points for modeling through mathematical analysis. The extracted information is then processed using a visual programming algorithm, imported into the VPL environment, and used for automated modeling. Initial results from an ongoing experiment on the automated modeling of vaults using point clouds from surveys are presented.