AligNeRF: High-Fidelity Neural Radiance Fields via Alignment-Aware Training

Neural Radiance Fields (NeRF or NeRFs) are to date emerging as a novel method for synthesizing novel views of complex 3D scenes, leveraging an artificial neural network to optimize a volumetric scene function using a set of input views. We conduct a preliminary critical review of the scientific and technical literature on NeRFs, and we highlight possible applications of the latter in the Cultural Heritage domain, for the image-based reconstruction of 3D models of real, multi-scale objects, even in combination with the more well-established photogrammetric techniques. A comparison is made between NeRFs and photogrammetry in terms of operating procedures and outputs (volumetric renderings vs. point clouds or meshes). It is demonstrated that NeRFs could be conveniently used for rendering objects (sculptures, archaeological remains, sites, paintings etc.) that are challenging for photogrammetry, typically: i) metallic, translucent, and/or transparent surfaces; ii) objects that present homogeneous textures; iii) occlusions, vegetation, and elements of very fine detail.