Improvements in NeRF

Abstract

Neural Radiance Fields (NeRF) represent a groundbreaking technique at the intersection of classical computer graphics and deep learning. It facilitates the generation of 3D objects from 2D images by employing an interpolation approach to produce novel 3D reconstructed views of intricate scenes. In contrast to traditional methods that directly reconstruct entire 3D scene geometry, NeRF utilizes a volumetric representation known as a “radiance field.” This field enables the generation of color and density for every point within the relevant 3D space. As NeRF is a relatively recent technique, ongoing efforts are focused on exploring and refining its capabilities and limitations. This paper reviews the deficiencies of the original NeRF and introduces methods aimed at addressing these shortcomings.