A comparative framework for neural rendering and photogrammetry for spherical images

Abstract Heritage 3D Reconstruction is a digital bridge to the past, providing a vivid window into history by meticulously preserving the intricate details of cultural artifacts and sites and bringing them to life for future generations to explore and appreciate. In recent years, novel Neural Rendering methods, such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), have shown promising results for 3D reconstruction. Despite their rapid development, a systematic comparison of these approaches in the context of $$360^{\circ }$$ imagery, particularly within Digital Cultural Heritage (DCH), remains largely unexplored. In this work, we present a novel comparative framework specifically designed to address this gap, performing a comprehensive assessment of traditional $$360^{\circ }$$ photogrammetry against emerging Neural Rendering approaches. To validate its effectiveness, we conducted a comparative analysis on two different Cultural Heritage scenarios: a large-scale outdoor environment and an object-centered setting. These case studies, which present a variety of challenges in the DCH domain, were selected to evaluate the ability of photogrammetry, NeRF, and 3DGS-based methods to reconstruct and render complex scenes in terms of both geometric accuracy and visual quality. The results highlight the differences between Neural Rendering approaches and conventional methods, particularly in handling spherical geometry, adaptability to varying lighting conditions and efficiency in data acquisition and processing. These findings confirm the continued robustness of spherical photogrammetry for metric reconstruction, while also underscoring the potential of Neural Rendering-based scene representation methods as promising tools for DCH preservation.