Nerfstudio: A Modular Framework for Neural Radiance Field Development
- URL: http://arxiv.org/abs/2302.04264v4
- Date: Tue, 17 Oct 2023 03:34:15 GMT
- Title: Nerfstudio: A Modular Framework for Neural Radiance Field Development
- Authors: Matthew Tancik, Ethan Weber, Evonne Ng, Ruilong Li, Brent Yi, Justin
Kerr, Terrance Wang, Alexander Kristoffersen, Jake Austin, Kamyar Salahi,
Abhik Ahuja, David McAllister, and Angjoo Kanazawa
- Abstract summary: Nerfstudio is a modular PyTorch framework for implementing Neural Radiance Fields (NeRF) methods.
NeRF is a rapidly growing area of research with wide-ranging applications in computer vision, graphics, robotics, and more.
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.
- Score: 60.210943944285184
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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