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.
Augmented Street Art: a Critical Contents and Application Overview
Street art is a growing phenomenon. The frequent appearance of works, projects, and events in this area reveals its increasing social and cultural role worldwide. The chance of digitizing art represents a benefit to defining cultural paths on the territory, providing an additional tool to understand and interpret it. Street art is characterized by peculiar aspects that make it unique in the artistic panorama. The democratization of contents and the physical decay of the work are two pillars. Any digitalization and communication project should consider them carefully, proposing a knowledge model respectful of the art. Augmented Reality (AR) is a representation tool that leads to achieving that delicate bal-ance between the real and the digital, enhancing the specificities of both. The authors start from the experimentation about artwork digitalization, connecting image deterioration with image recognition. Besides, they show some possible applications in Rome through a critical analysis of the domain, open-ing some future multidisciplinarity scenarios.