NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections
Abstract
We present a learning-based method for synthesizingnovel 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 multi-layer 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.
Cite
Text
Martin-Brualla et al. "NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections." Conference on Computer Vision and Pattern Recognition, 2021. doi:10.1109/CVPR46437.2021.00713Markdown
[Martin-Brualla et al. "NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections." Conference on Computer Vision and Pattern Recognition, 2021.](https://mlanthology.org/cvpr/2021/martinbrualla2021cvpr-nerf/) doi:10.1109/CVPR46437.2021.00713BibTeX
@inproceedings{martinbrualla2021cvpr-nerf,
title = {{NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections}},
author = {Martin-Brualla, Ricardo and Radwan, Noha and Sajjadi, Mehdi S. M. and Barron, Jonathan T. and Dosovitskiy, Alexey and Duckworth, Daniel},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2021},
pages = {7210-7219},
doi = {10.1109/CVPR46437.2021.00713},
url = {https://mlanthology.org/cvpr/2021/martinbrualla2021cvpr-nerf/}
}