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（NeRF）是一个快速发展的研究领域，在计算机视觉、图形学、机器人学等领域具有广泛的应用前景。为简化NeRF研究的开发与部署流程，我们提出了一个模块化的PyTorch框架——Nerfstudio。该框架包含即插即用的组件，可用于实现基于NeRF的方法，使研究人员和从业者能够轻松地将NeRF整合到其项目中。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流程，以及将数据导出为视频、点云和网格表示的工具。Nerfstudio的模块化特性促成了我们方法Nerfacto的开发，该方法结合了近期论文中的组件，在速度与质量之间取得平衡，同时保持对未来改进的灵活性。为推动社区驱动开发，所有相关代码与数据均通过开源许可公开发布于https://nerf.studio。