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.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Geometry Reconstruction | Synthetic Objects (test) | Chamfer Distance (CD)0.0404 | 24 | |
| Novel View Synthesis | Mip-NeRF 360 4x downsampled (test) | PSNR27.98 | 8 | |
| Hyperspectral Novel View Synthesis | Surface Optics (SOP) Datasets (test) | PSNR16.37 | 8 | |
| Hyperspectral Novel View Synthesis | BaySpec | PSNR19.12 | 8 | |
| Multi-view Consistent Inpainting | NeRF Synthetic | PSNR7.76 | 7 | |
| 3D Scene Completion | NeRF synthetic dataset 10 scenes (test) | PSNR14.71 | 6 | |
| View Synthesis | Pandaset Interpolation | PSNR27.122 | 5 | |
| View Synthesis | Pandaset Lane Shift | FID (2m)116.7 | 5 | |
| Novel View Synthesis | Farmland (FL) UAV-captured scene (test) | PSNR20.99 | 5 | |
| Synthetic Image Quality Evaluation | GT Scene (test) | PSNR22.736 | 3 |