Recursive-NeRF: An Efficient and Dynamically Growing NeRF
Guo-Wei Yang, Wen-Yang Zhou, Hao-Yang Peng, Dun Liang, Tai-Jiang Mu, Shi-Min Hu
5/19/2021
Keywords: Sampling, Coarse-to-Fine
Venue: ARXIV 2021
Bibtex:
@article{yang2021recursivenerf,
journal = {arXiv preprint arXiv:2105.09103},
booktitle = {ArXiv Pre-print},
author = {Guo-Wei Yang and Wen-Yang Zhou and Hao-Yang Peng and Dun Liang and Tai-Jiang Mu and Shi-Min Hu},
title = {Recursive-NeRF: An Efficient and Dynamically Growing NeRF},
year = {2021},
url = {http://arxiv.org/abs/2105.09103v1},
entrytype = {article},
id = {yang2021recursivenerf}
}
Abstract
View synthesis methods using implicit continuous shape representations learned from a set of images, such as the Neural Radiance Field (NeRF) method, have gained increasing attention due to their high quality imagery and scalability to high resolution. However, the heavy computation required by its volumetric approach prevents NeRF from being useful in practice; minutes are taken to render a single image of a few megapixels. Now, an image of a scene can be rendered in a level-of-detail manner, so we posit that a complicated region of the scene should be represented by a large neural network while a small neural network is capable of encoding a simple region, enabling a balance between efficiency and quality. Recursive-NeRF is our embodiment of this idea, providing an efficient and adaptive rendering and training approach for NeRF. The core of Recursive-NeRF learns uncertainties for query coordinates, representing the quality of the predicted color and volumetric intensity at each level. Only query coordinates with high uncertainties are forwarded to the next level to a bigger neural network with a more powerful representational capability. The final rendered image is a composition of results from neural networks of all levels. Our evaluation on three public datasets shows that Recursive-NeRF is more efficient than NeRF while providing state-of-the-art quality. The code will be available at https://github.com/Gword/Recursive-NeRF.
Citation Graph
(Double click on nodes to open corresponding papers' pages)
(Double click on nodes to open corresponding papers' pages)
* Showing citation graph for papers within our database. Data retrieved from Semantic Scholar. For full citation graphs, visit ConnectedPapers.