NeRF++: Analyzing and Improving Neural Radiance Fields
Kai Zhang, Gernot Riegler, Noah Snavely, Vladlen Koltun
10/15/2020
Keywords: Fundamentals, Sampling
Venue: ARXIV 2020
Bibtex:
@article{zhang2020nerf++,
journal = {arXiv preprint arXiv:2010.07492},
booktitle = {ArXiv Pre-print},
author = {Kai Zhang and Gernot Riegler and Noah Snavely and Vladlen Koltun},
title = {NeRF++: Analyzing and Improving Neural Radiance Fields},
year = {2020},
url = {http://arxiv.org/abs/2010.07492v2},
entrytype = {article},
id = {zhang2020nerf++}
}
Abstract
Neural Radiance Fields (NeRF) achieve impressive view synthesis results for a variety of capture settings, including 360 capture of bounded scenes and forward-facing capture of bounded and unbounded scenes. NeRF fits multi-layer perceptrons (MLPs) representing view-invariant opacity and view-dependent color volumes to a set of training images, and samples novel views based on volume rendering techniques. In this technical report, we first remark on radiance fields and their potential ambiguities, namely the shape-radiance ambiguity, and analyze NeRF's success in avoiding such ambiguities. Second, we address a parametrization issue involved in applying NeRF to 360 captures of objects within large-scale, unbounded 3D scenes. Our method improves view synthesis fidelity in this challenging scenario. Code is available at https://github.com/Kai-46/nerfplusplus.
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