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.

翻译：神经辐射场（Neural Radiance Fields, NeRF）是计算机视觉、图形学、机器人学等领域中一个快速发展的研究方向，具有广泛的应用前景。为简化NeRF研究的开发与部署流程，我们提出一个基于PyTorch的模块化框架——Nerfstudio。该框架包含即插即用的组件，用于实现基于NeRF的方法，使研究人员和从业者能够轻松地将NeRF集成到其项目中。此外，模块化设计支持丰富的实时可视化工具、用于导入野外采集数据的简化管线，以及将结果导出为视频、点云和网格表示的工具。通过Nerfstudio的模块化特性，我们进一步开发了Nerfacto方法——该方法整合近期论文中的组件，在速度与质量之间取得平衡，同时保持对未来修改的灵活性。为促进社区驱动开发，所有相关代码与数据均通过开源许可在https://nerf.studio上公开提供。