HBIM technologies demonstrate increased potential in managing existing built heritage, leading to improved building lifecycle engineering. However, applying HBIM protocols to Cultural Heritage, particularly museum assets, represents an outstanding question. The study provides the findings of a research project conducted at the Galleria Borghese Museum to implement a geometric and informative BIM-based digital environment for museum management, as well as preventive and predictive maintenance. Furthermore, the study examines—referring to the debate over the widespread application of integrated digital technologies to cultural heritage management—the opportunities and challenges associated with digitization processes towards the implementation of Digital Twin (DT) (Vuoto in Int. J. Arch. Herit. 18(11), 1762–1795, 2024) and Digital Cultural Objects (DCO), as well as the transferability of the study’s findings. The Galleria Borghese Museum provides scholars with the opportunity to examine architecture and artworks integrated into spaces of both permanent and temporary exhibitions, multidisciplinary study areas, restoration spaces for art and architecture, and environments conceived for the valorization, communication, and participation of a large public of experts and non-experts.
Generation and Evaluation of High-Fidelity Digital Twins: An All-Inclusive Pipeline for Enhanced Construction Efficiency in Diverse VR Environments
XR (extended reality) environments have driven professionals in the construction industry to adopt advanced digital surveying and 3D modelling techniques, improving project quality and site management through enhanced visualization, accuracy, and precision. Photogrammetry and laser scanning have been crucial in the scan-to-XR process, enabling the development of digital twins (DT) throughout the construction lifecycle. However, converting survey data into usable models for immersive experiences requires expertise in digital representation and software development. Digital representation transforms raw data into functional models, yet challenges remain. Survey outputs typically feature high polygon counts for detail, which can overload XR applications, causing slowdowns and reducing fluidity, especially on devices with limited resources. Additionally, high-resolution textures further strain computational power and memory. Optimising these textures is key to balancing visual quality and performance. XR platforms like Unity and Unreal Engine demand specific rendering standards, and non-optimised models can fail to meet these, requiring further adjustments. This study aims to develop a pipeline for creating high-fidelity DTs of an ongoing construction site. Data is collected through photogrammetry and laser scanning, followed by model optimisation for XR applications. The optimised model is integrated into real-time platforms for interactive use, producing both VR and web-XR models. This pipeline will aid in construction-site management, safety inspections, and communication between stakeholders, contributing to DT technology and to the efficiency of the construction industry.
Built Heritage Adapted Information Management Through AI. The AIM-EBIM Project
The paper is focused on an ongoing project funded by the Emilia-Romagna region and aimed at the creation of a new workflow finalizing digital data from integrated survey towards an “adaptive” Building Information Modeling (BIM). The project AIM-eBIM—Adapted Information Management for existing Buildings Information Modeling—brings together regional research laboratories and companies to pursue industrial research topics towards a greater deployment of digital tools. Digital surveying has triggered huge potential for innovation, but also generated new challenges in managing and using large amounts of data, often left unused. The quantity of surveyed data used to document built or Cultural Heritage often does not correspond to the quality or reliability of information. Moreover, parametric modeling of existing heritage through BIM is becoming as pervasive as it is necessary, considering regulatory trends. However, these tools can be ineffective from the point of view of users (professionals, companies) who must deal with such complexity. The challenge is to bring discretization (and simplification) processes to source data toward easier informative integration into BIM models, by facilitating and enhancing interpretation needs. In this direction, Artificial Intelligence (AI) algorithms are part of the process. The adapted informative implementation of parametric models is based on digital source data (laser and photogrammetry) segmentation by AI according to specific topics (documentation, analysis, monitoring, conservation, project) and criteria (materials, techniques, components, structures).
Advanced Deviation Analysis Visualization for BIM in Heritage Environment
This paper presents a methodology leveraging modern technologies such as BIM, laser scanning, and virtual reality to address challenges in maintaining historical buildings. The focus is on a scan-vs-BIM deviation analysis workflow, enabling the identification of modelling inaccuracies and structural issues by comparing HBIM models with point-cloud surveys. The proposed approach facilitates decision-making processes by providing an accessible and detailed visualization of results. The methodology begins with a structured BIM model and a corresponding point cloud. Using Autodesk Revit and a tailored Dynamo script, distances between building elements and their surveyed points are calculated and organized for compatibility with various platforms. The results are then transformed into interactive 3D visualizations using Python, where points are spatially and color-coded based on deviation values. The workflow integrates immersive technologies, such as virtual reality, to explore the results interactively and at real scale, enhancing insights beyond traditional 2D graphs and screen-based methods. This immersive visualization highlights critical details and supports improved decision-making in structural analysis and conservation efforts. The paper also discusses the potential for augmented reality to further enhance these visualizations, offering direct comparisons between BIM models, point clouds, and the physical building. With a BIM-based and open-source approach, the methodology ensures broad accessibility and reusability, making it a robust tool for deviation analysis and visualization in heritage conservation projects.
Automated Scan-To-BIM Methodology for Accurate 3D Modeling of Embedded MEP Systems
This paper presents an integrated methodology to enhance the detection, data acquisition, and modeling of embedded MEP systems in existing buildings, addressing the gap between as-built information and the need for accurate simulation models. The approach begins with a comprehensive digital survey of existing structures using LiDAR and Radar technologies to generate point clouds and accurately tag the coordinates of detected installations. These datasets serve as the foundation for constructing a precise 3D model in an EBIM environment. To automate data integration, a custom API was developed to cross-reference point-cloud coordinates with manual detections, ensuring accurate representation of all embedded systems. The resulting enriched EBIM model is further enhanced by importing it into the Unity game engine. Through the Vuforia augmented reality SDK, an XR experience was created, offering an immersive and interactive visualization of the 3D model and detailed MEP systems. This methodology demonstrates the potential of integrating advanced digital surveying, EBIM, and XR technologies to streamline building surveys and 3D model creation. The proposed approach not only improves accuracy and efficiency but also introduces innovative tools for design, construction, and maintenance workflows.
Scan-To-BIM-To-VR Processes for the Documentation and Valorization of the Defensive Fortifications in Piombino
The paper intends to explore the integration between Scan-to-BIM parametric modelling techniques and VR virtual systems to support activities of documentation, analysis, valorization, and popular storytelling of Architectural Heritage. In this sense, these themes have been deepened through a series of experimental HBIM applications conducted on the military fortifications of Piombino, and more specifically, on the case study of the defensive complex formed by the Rivellino and the Porta a Terra. Through the implementation of the HBIM model within game-engine platforms, the project aims at enhancing the historical heritage, favouring dissemination and interactive use through immersive virtual environments.
Reliability of Human-genAI Integrated Process for the Virtual Reconstruction of a Lost Architecture
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.
Artistry, Technology and Challenges: The Subtle Balance Between Fake Results and Real Integration in the Use of AI for Image Generation on Medieval Frescos Reconstructions
AI-based image-generative tools significantly enhance creativity and digital reconstruction by accelerating workflows for reconstructing digital heritage. These tools apply to various contexts, such as archaeological sites, transformed urban areas, damaged buildings, unbuilt architecture, and partially lost artworks like mural paintings and frescoes. In reconstructing such works, the process is highly complex and requires scholars with expertise in recognizing and attributing fragments and better-preserved pieces. While it remains necessary to suggest potential lines, figures, or scenes, the outcome often involves multiple possibilities. AI image generators can support these challenging tasks by integrating them into workflows, from producing stochastic results to extending patterns and coloured areas. However, their use, especially via “off-the-shelf” software, introduces two key challenges. The first concerns achieving accurate reconstructions that maximize the AI’s potential while avoiding casual or tentative outputs. This involves addressing limitations in existing AI systems and ensuring the final results are both reliable and time efficient. The second challenge relates to cultural evaluation. The line between AI-assisted reconstruction and creating entirely fake results is delicate. Misuse could exploit public fascination with AI or lead to errors and misinterpretations without expert oversight. This integration is in its early stages, necessitating rigorous testing and exploration. The proposed contribution analyzes the digital reconstruction of medieval frescoes from central Italy, blending traditional methods with significant AI inputs. These case studies highlight the balance between human-driven processes and the acceptance of digital outputs, offering reflections on the evolving relationship between human expertise and AI-driven creativity.
Advanced Digitization of Cultural Heritage via NeRF
Digitization processes, increasingly employed in various ways for the accessibility and preservation of cultural heritage, continuously engage with the diverse range of materials that define it. Objects like reflective ceramics and transparent glass have complex optical properties, which pose a unique challenge for digital acquisition methods such as photogrammetry and laser scanning, as well as for the modelling process. These materials often generate specular reflections or refract light, which interfere with the conventional algorithms used in the acquisition processes, leading to incomplete or inaccurate 3D models. This study explores the potential of Neural Radiance Fields (NeRF), an innovative 3D reconstruction technique based on deep learning, to overcome these limitations. By using volumetric encoding of scenes and simulating complex light interactions, NeRF captures phenomena like reflections and refractions with consistent realism. The exploration is carried out through a stress test on ceramic and glass materials, using both NeRF technology and traditional systems like digital photogrammetry. The comparison of the results highlights the advantages and disadvantages of both technologies, while emphasizing the current need for their complementarity, with workflows still largely hybrid.
AI-Enhanced UAV Photogrammetry Point Clouds for Cultural Heritage Assessment
Advancements in low-altitude remote sensing and image analysis have revolutionized the digitisation of real-world objects, initially represented as point clouds. Over the past decade, drone-based surveying has gained traction; however, noise during data capture and 3D reconstruction remains a critical challenge, affecting the accuracy and usability of UAV images in real-world applications. This study presents a novel approach to enhance cultural heritage assessment using deep learning models based on the Autoencoder Gaussian Mixture Clustering Model and the Autoencoder K-means Clustering Model. These models improve the accuracy of point clouds generated from UAV images for better condition assessment and survey. The research focuses on photogrammetric accuracy parameters for clustering point clouds. The new approach chooses K-means for finding global patterns due to its robust accuracy, and the Gaussian Mixture Clustering Model for local changes and inspection applications. Moreover, these models investigate the accuracy of point cloud clusters generated from K-means and the Gaussian Mixture Clustering Model. To validate the new approach, the study evaluates variations in key parameters under diverse built-environment conditions using the Temple of Neptune point cloud in Paestum, Italy. The results demonstrate significant improvements in point cloud 3D reconstruction, leading to more accurate surveys and assessments. The proposed method surpasses traditional techniques, offering enhanced applicability for point-cloud filtering. Furthermore, comparisons of clustering results highlight the algorithm, establishing its promising potential for advancing UAV-based surveying and inspection practices.