The paper investigates evaluation methodologies for AI-based virtual reconstruction of historical architecture, focusing on the uncertainty inherent in generative predictions. The research critically compares traditional analog reconstruction workflows with AI-driven reconstruction processes based on GAN networks, proposing a conceptual framework called the “Uncertainty Tree” to describe the decision-making chain involved in reconstruction. The study develops a qualitative evaluation methodology intended to complement quantitative pixel-based assessments by introducing prediction thresholds ranging from impossible to highly probable reconstructions. Through experiments on datasets of Greek Doric temples and Mudejar Romanesque churches, the research argues that specialized datasets and expert qualitative evaluation significantly improve the plausibility and reliability of AI-generated architectural reconstructions. The paper ultimately proposes a new epistemological framework for evaluating AI predictions in heritage reconstruction, emphasizing human supervision, dataset specificity, and iterative qualitative assessment.
Automatic Virtual Reconstruction of Historic Buildings Through Deep Learning. A Critical Analysis of a Paradigm Shift
This paper critically examines the emerging use of artificial intelligence for the automatic virtual reconstruction of historic buildings, comparing it with traditional heritage reconstruction methodologies. The authors trace the historical evolution of reconstruction practices—from manual drawing, archaeological interpretation, CAD modelling, photogrammetry, and BIM—to current AI-based approaches founded on Deep Learning. The study focuses on Generative Adversarial Networks (GANs) trained to infer missing architectural parts from ruined structures, using synthetic datasets of Greek temples represented in multiple ruin states and reconstructed versions. A complementary Natural Language Processing workflow is also tested to improve segmented image generation and automate parts of the training process. Results suggest that AI can identify hidden formal and constructive patterns, generate multiple predictive alternatives, and support specialists in evaluating reconstruction hypotheses. At the same time, the paper warns that neural networks simplify architectural diversity into generalized stylistic rules and still depend on carefully designed datasets. The authors conclude that traditional scholarly expertise and AI prediction systems will likely coexist, marking a paradigm shift in how virtual reconstruction may be practiced in the near future.
Perspective Between Representation and AR: the Apse of the Church of St. Ignatius
This contribution studies the ways in which Augmented Reality (AR) can communicate and disseminate knowledge about our cultural heritage in general and, in particular, Architectural Perspectives (AP). The focus is to increase our knowledge and understanding of perspective technique and how the latter deals with the unique mix between the real architectural environment and an illusory space, in this case the decorations of the presbytery and apse of the Church of St. Ignatius in Rome. The specific objective of the study is the possibility to use the Pozzo’s treatise to “augment” its fruition, accompanied by a virtual visit in order to gather more in-depth information about the perspective construction that creates the effect of spatial expansion thanks to this expedient. This decoration is in fact one of the rare cases of a perspective representation of a pictorial project, in turn also a perspective designed by Pozzo; this design is the “activation” element of an AR experience that immediately illustrates the perspective bravura of its creator.
Wood Inlays and AR: Considerations Regarding Perspective
The contribution reflects on the options provided by recent Augmented Reality (AR) applications to the knowledge and enhancement of a nationally and internationally renowned cultural asset: perspec-tive wood inlays.The methodology envisages two closely–connected stages initially involving perspective decoding and ensuing reconstruction of the illusory model, followed by the creation of a set–up for the AR pursuant to the digitalization of the three–dimensional model.In AR, perspective is the ideal tool to virtually experience the space represented in the decoration; at the same time it provides the most suitable solutions required to optimise the AR project.