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NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections | | Ricardo Martin-Brualla
; Noha Radwan
; Mehdi S. M. Sajjadi
; Jonathan T. Barron
; Alexey Dosovitskiy
; Daniel Duckworth
; | | Date: |
5 Aug 2020 | | Abstract: | We present a learning-based method for synthesizing novel views of complex
outdoor 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 implicitly model the volumetric density and color of a
scene. 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. In this work, we introduce a series of extensions to NeRF to address
these issues, thereby allowing for accurate reconstructions from unstructured
image collections taken from the internet. We apply our system, which we dub
NeRF-W, to internet photo collections of famous landmarks, thereby producing
photorealistic, spatially consistent scene representations despite unknown and
confounding factors, resulting in significant improvement over the state of the
art. | | Source: | arXiv, 2008.02268 | | Services: | Forum | Review | PDF | Favorites | |
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