NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections
This enables accurate 3D reconstructions from uncontrolled internet photos, addressing real-world challenges for computer vision and graphics applications.
The paper tackled the problem of synthesizing novel views from unstructured photo collections with variable illumination and transient occluders, achieving significantly closer to photorealism than prior state-of-the-art methods.
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.