NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections

Autor:	Martin-Brualla, Ricardo, Radwan, Noha, Sajjadi, Mehdi S. M., Barron, Jonathan T., Dosovitskiy, Alexey, Duckworth, Daniel
Rok vydání:	2020
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Graphics Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Project website: https://nerf-w.github.io. Ricardo Martin-Brualla, Noha Radwan, and Mehdi S. M. Sajjadi contributed equally to this work. Updated with results for three additional scenes
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2008.02268 Zobrazit plný text záznamu View this record from Arxiv