id
	13134
Url
	https://chloe.cnr.it/s/BiDiAr/item/13134
Resource template
	Conference Paper
Resource class
	fabio:ConferencePaper
Title
	Nerfstudio: A Modular Framework for Neural Radiance Field Development
Creator
	Tancik, Matthew
	Weber, Ethan
	Ng, Evonne
	Li, Ruilong
	Yi, Brent
	Kerr, Justin
	Wang, Terrance
	Kristoffersen, Alexander
	Austin, Jake
	Salahi, Kamyar
	Ahuja, Abhik
	McAllister, David
	Kanazawa, Angjoo
Date
	2023
Language
	eng
Abstract
	Neural Radiance Fields (NeRF) are a rapidly growing area of research with wide-ranging applications in computer vision, graphics, robotics, and more. In order to streamline the development and deployment of NeRF research, we propose a modular PyTorch framework, Nerfstudio. Our framework includes plug-and-play components for implementing NeRF-based methods, which make it easy for researchers and practitioners to incorporate NeRF into their projects. Additionally, the modular design enables support for extensive real-time visualization tools, streamlined pipelines for importing captured in-the-wild data, and tools for exporting to video, point cloud and mesh representations. The modularity of Nerfstudio enables the development of Nerfacto, our method that combines components from recent papers to achieve a balance between speed and quality, while also remaining flexible to future modifications. To promote community-driven development, all associated code and data are made publicly available with open-source licensing at https://nerf.studio.
Is Part Of
	Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Proceedings
Doi
	https://doi.org/10.1145/3588432.3591516
Pages
	1-12
Uri
	http://arxiv.org/abs/2302.04264
Homepage
	https://www.zotero.org/groups/5293298/bidiar/items/T6NLVCWF/item-list

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