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.

本论文提出了一种基于学习的方法，利用无组织的野外照片合成复杂场景的新视图，并介绍了一系列扩展 NeRF 的方法以解决自然图像中的一些问题，最终成功地构建了名称为 NeRF-W 的系统，能够从互联网上的无组织照片集合中重建场景并呈现接近于照片现实主义水平的合成视图。