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Nerfstudio: A Modular Framework for Neural Radiance Field Development

About

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.

Matthew Tancik, Ethan Weber, Evonne Ng, Ruilong Li, Brent Yi, Justin Kerr, Terrance Wang, Alexander Kristoffersen, Jake Austin, Kamyar Salahi, Abhik Ahuja, David McAllister, Angjoo Kanazawa• 2023

Related benchmarks

TaskDatasetResultRank
Geometry ReconstructionSynthetic Objects (test)
Chamfer Distance (CD)0.0404
24
Novel View SynthesisMip-NeRF 360 4x downsampled (test)
PSNR27.98
8
Hyperspectral Novel View SynthesisSurface Optics (SOP) Datasets (test)
PSNR16.37
8
Hyperspectral Novel View SynthesisBaySpec
PSNR19.12
8
Multi-view Consistent InpaintingNeRF Synthetic
PSNR7.76
7
3D Scene CompletionNeRF synthetic dataset 10 scenes (test)
PSNR14.71
6
View SynthesisPandaset Interpolation
PSNR27.122
5
View SynthesisPandaset Lane Shift
FID (2m)116.7
5
Novel View SynthesisFarmland (FL) UAV-captured scene (test)
PSNR20.99
5
Synthetic Image Quality EvaluationGT Scene (test)
PSNR22.736
3
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