A Comparative Study of Traditional Light Field Methods and NeRF

Abstract

Neural Radiance Fields (NeRF) is a recent technology which had a large impact in computer vision, promising to generate high quality novel views and corresponding disparity map, all using a fairly small number of input images. In effect, they are a new way to represent a light field. In this paper, we compare NeRF with traditional light field methods for novel view synthesis and depth estimation, in an attempt to quantify the advantages brought by NeRF, and to put these results in perspective with the way both paradigms are used practically. We provide qualitative and quantitative comparisons, discuss them and highlight some aspects of working with NeRF depending on the type of light field data used.