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.
Exploring Rapid 3D Heritage Asset Documentation: A Comparative Study of Laser Scanning and NeRF Algorithms in Museum Reconstruction
This paper explores the use of digital techniques for rapid surveys in cultural heritage, focusing on the creation of virtual environments for educational purposes. It compares two prominent methods for 3D reconstruction: laser scanning and AI-enhanced photogrammetry, particularly Neural Radiance Fields (NeRF). The case study centers on the virtual reconstruction of the Eccel Kreuzer Museum in Bolzano, South Tyrol, which involved documenting its original layout before an exhibition change. The research evaluates the performance of the Leica BLK laser scanner against the NeRF-based 3D models generated from video footage captured with an Insta360 ONE camera. While laser scanning provides high-quality results, the NeRF method, leveraging AI-based algorithms, offers a faster, cost-effective solution, particularly in environments with reflective surfaces and confined spaces. Despite challenges in accuracy and computational demands, the AI approach proves suitable for rapid documentation, especially when precision is less critical. The study highlights the advantages and limitations of both techniques, contributing to the ongoing development of 3D digital heritage assets that balance speed, cost, and quality in various applications such as VR and AR educational environments.
Integrated Level Design Generation Methodology for Virtual Exploration in XR Mode
In recent years, digital technologies have become increasingly important in preserving and promoting cultural heritage. Virtual recreations of historical spaces offer immersive experiences that bridge the past and present or address the issue of inaccessible locations. However, digitizing real sites for use in applied games poses technical, methodological, and stylistic challenges, particularly concerning interactivity and virtual exploration. This paper details the digitization of the historic center of Brixen for a serious game aimed at heritage promotion, based on a project developed during the 2023 UID Summer School led by Prof. Luigini and Prof. Rossi. Instead of relying on concept art and photographs, the project used real-world elements optimized for real-time rendering (RTR) to achieve photorealism within a digital environment. The objective is not to create an exact replica, but rather to evoke the atmosphere and memory of the place within the game experience.
Rapid and Low-Cost 3D Model Creation Using Nerf for Heritage Videogames Environments
The paper explores the use of Neural Radiance Fields (NeRF) for the rapid and low-cost creation of 3D heritage environments intended for educational videogames. The research focuses on reconstructing architectural heritage using 360-degree images and videos processed through Nerfstudio and Nerfacto workflows. Compared with traditional photogrammetry and laser scanning, the proposed approach aims to reduce acquisition effort while maintaining visually convincing reconstructions. The study demonstrates how NeRF-based pipelines can generate point clouds and meshes suitable for immersive heritage education, while also discussing limitations related to computational requirements and reconstruction resolution.
Neural Networks as an Alternative to Photogrammetry. Using Instant NeRF and Volumetric Rendering
This paper investigates Neural Radiance Fields (NeRF) as an emerging alternative to traditional photogrammetry for the digital acquisition of heritage objects and environments. The study reviews recent developments in machine learning and computer vision, focusing on NVIDIA Instant NeRF, volumetric rendering, and related platforms such as Luma AI and Nerfstudio. Unlike conventional photogrammetry, NeRF systems reconstruct scenes through neural networks that infer missing views and generate volumetric representations with realistic lighting, reflections, and textures. The authors test these methods on a sculptural case study, evaluating speed, geometric quality, mesh extraction, texture generation, and interoperability with external software such as Blender and Unreal Engine. Results show that NeRF workflows can reduce acquisition and processing times while performing particularly well on reflective materials and complex lighting conditions, areas where photogrammetry often struggles. Although current outputs still present limitations in mesh closure, topology control, and metric reliability, the research suggests that neural rendering may soon become a powerful tool for digital twins, immersive heritage visualization, and future survey practices.
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.