NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections

Understand

We present a learning-based method for synthesizing novel views of complex scenes using only unstructured collections of in-the-wild photographs.

• We build on Neural Radiance Fields (NeRF), which uses the weights of a multilayer perceptron to model the density and color of a scene as a function of 3D coordinates.
• While NeRF works well on images of static subjects captured under controlled settings, it is incapable of modeling many ubiquitous, real-world phenomena in uncontrolled images, such as variable illumination or transient occluders.
• We introduce a series of extensions to NeRF to address these issues, thereby enabling accurate reconstructions from unstructured image collections taken from the internet.