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 Text-To-Image Procedure for the Visualization of Figurative and Literary Tòpoi
The paper proposes a workflow for translating literary and figurative textual descriptions into AI-generated images through text-to-image neural networks. The research combines linguistic analysis, lexical semantics, prompt engineering, and Stable Diffusion-based image generation to investigate the relationship between verbal and visual representation. Drawing on theories of visual culture, ekphrasis, and Aby Warburg’s Mnemosyne Atlas, the study develops a methodological framework for guiding neural networks through semantic keywords, syntactic structures, contextual references, and prompt modulation. The workflow is tested on literary and architectural texts from different historical periods, including utopian cities, nineteenth-century urban descriptions, and imaginary urban narratives. The research demonstrates how AI image generation can support the visualization of literary spatial imaginaries while also revealing the ambiguities, arbitrariness, and interpretative challenges inherent in translating text into visual form.
V.A.I. Reality. A Holistic Approach for Industrial Heritage Enhancement
The post–industrial heritage, characterized by heterogeneous, tangible and intangible factors, requires digital tools and a holistic approach to undertake the most appropriate enhancement process. Cur-rent virtual realities (Vr, Ar, Ir) allow the modeling of physical environments and the management and virtualization of a large and varied amount of data, thus helping to better understand the complex-ity of the real phenomenon. The paper proposes a method, named D.V.M.R. (acronym for Design, Virtualization, Modeling, Reproduction), which in four temporally consequential phases builds a tool capable of providing territorial, environmental, architectural and historical information of a case study. The method was applied for the design of a reuse of the brick factory, known as ex Sieci and located in Scauri in southern Lazio, owned by the Municipality of Minturno. The factory, which looks like a majestic cathedral on the sea, had in the past and still has a significant centrality in the social life of the local inhabitants.