2020/08/05 by Ricardo Martin-Brualla, Martin-Brualla, Ricardo, Noha Radwan +9 · 117 citations
Computer Science · Environmental Science · #Advanced Vision and Imaging #Generative Adversarial Networks and Image Synthesis #Remote Sensing and LiDAR Applications #cs.CV #cs.GR #cs.LG
paper · pdf · doi:10.48550/arxiv.2008.02268
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
arxiv created 2021/01/06 · arxiv updated 2021/01/07
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.