  Neural Radiance Field (NeRF) has recently become a significant development in
the field of Computer Vision, allowing for implicit, neural network-based scene
representation and novel view synthesis. NeRF models have found diverse
applications in robotics, urban mapping, autonomous navigation, virtual
reality/augmented reality, and more. Due to the growing popularity of NeRF and
its expanding research area, we present a comprehensive survey of NeRF papers
from the past two years. Our survey is organized into architecture and
application-based taxonomies and provides an introduction to the theory of NeRF
and its training via differentiable volume rendering. We also present a
benchmark comparison of the performance and speed of key NeRF models. By
creating this survey, we hope to introduce new researchers to NeRF, provide a
helpful reference for influential works in this field, as well as motivate
future research directions with our discussion section.
