Nerfstudio: A Modular Framework for Neural Radiance Field Development
Nerfstudio: A Modular Framework for Neural Radiance Field Development
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DOI：
10.1145/3588432.3591516
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发表时间：
2023-02
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通讯作者：
Matthew Tancik;Ethan Weber;Evonne Ng;Ruilong Li;Brent Yi;J. Kerr;Terrance Wang;Alexander Kristoffersen;J. Austin;Kamyar Salahi;Abhik Ahuja;David McAllister;Angjoo Kanazawa
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作者：
Matthew Tancik;Ethan Weber;Evonne Ng;Ruilong Li;Brent Yi;J. Kerr;Terrance Wang;Alexander Kristoffersen;J. Austin;Kamyar Salahi;Abhik Ahuja;David McAllister;Angjoo Kanazawa
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、机器人等领域有着广泛的应用。为了简化NeRF研究的开发和部署，我们提出了一个模块化的PyTorch框架Nerfstudio。我们的框架包括用于实现基于NeRF的方法的即插即用组件，这使得研究人员和从业人员可以轻松地将NeRF纳入他们的项目。此外，模块化设计支持广泛的实时可视化工具，用于导入捕获的野外数据的简化管道，以及用于导出到视频，点云和网格表示的工具。Nerfstudio的模块化使Nerfacto的开发成为可能，我们的方法结合了最近论文中的组件，以实现速度和质量之间的平衡，同时也保持了对未来修改的灵活性。为了促进社区驱动的开发，所有相关的代码和数据都通过开源许可公开提供。
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.