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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
Novel View SynthesisLLFF
PSNR13.7
130
Novel View SynthesisNeRF Synthetic (test)
PSNR29.19
46
Novel View SynthesisZJU-MoCap
PSNR26.2
34
Sparse-view SynthesisDL3DV Outdoor Benchmark (test)
PSNR13.61
24
Geometry ReconstructionSynthetic Objects (test)
Chamfer Distance (CD)0.0404
24
Novel View SynthesisDNA Rendering dataset (test)
Memory (GB)1.2
18
Discretized Goal NavigationReplica
Feasibility100
16
Semantic Instance TargetingReplica
Feasibility100
16
Depth ReconstructionWaymo interp.
MedL20.164
13
SDF ReconstructionReplica Office 0
SDF MAE (All) [cm]63.011
13
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