  Neural radiance fields (NeRF) are a groundbreaking computer vision technology
that enables the generation of high-quality, immersive visual content from
multiple viewpoints. This capability holds significant advantages for
applications such as virtual/augmented reality, 3D modelling and content
creation for the film and entertainment industry. However, the evaluation of
NeRF methods poses several challenges, including a lack of comprehensive
datasets, reliable assessment methodologies, and objective quality metrics.
This paper addresses the problem of NeRF quality assessment thoroughly, by
conducting a rigorous subjective quality assessment test that considers several
scene classes and recently proposed NeRF view synthesis methods. Additionally,
the performance of a wide range of state-of-the-art conventional and
learning-based full-reference 2D image and video quality assessment metrics is
evaluated against the subjective scores of the subjective study. The
experimental results are analyzed in depth, providing a comparative evaluation
of several NeRF methods and objective quality metrics, across different classes
of visual scenes, including real and synthetic content for front-face and
360-degree camera trajectories.
