Abstract
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. We apply our system, dubbed NeRF-W, to internet photo
collections of famous landmarks, and demonstrate temporally consistent novel
view renderings that are significantly closer to photorealism than the prior
state of the art.
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