模块化设计
计算机科学
模块化(生物学)
软件部署
领域(数学)
可视化
点云
光辉
软件工程
绘图
云计算
人工智能
机器人学
系统工程
数据科学
机器人
计算机图形学(图像)
程序设计语言
工程类
操作系统
生物
遗传学
光学
物理
纯数学
数学
作者
Matthew Tancik,Ethan Weber,Evonne Ng,Ruilong Li,Brent Yi,Terrance Wang,Alexander Kristoffersen,Jake Austin,Kamyar Salahi,Abhik Ahuja,David McAllister,Justin Kerr,Angjoo Kanazawa
标识
DOI:10.1145/3588432.3591516
摘要
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.
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