This work was presented at the IEEE International Conference on Robotics and Automation 2023 Workshop on Unconventional Spatial Representations. Neural radiance fields (NeRFs) are a class of implicit scene representations that model 3D environments from color images. NeRFs are expressive, and can model the complex and multi-scale geometry of real world environments, which potentially makes them a powerful tool for robotics applications. Modern NeRF training libraries can generate a photo-realistic NeRF from a static data set in just a few seconds, but are designed for offline use and require a slow pose optimization pre-computation step. In this work we propose NerfBridge, an open-source bridge between the Robot Operating System (ROS) and the popular Nerfstudio library for real-time, online training of NeRFs from a stream of images. NerfBridge enables rapid development of research on applications of NeRFs in robotics by providing an extensible interface to the efficient training pipelines and model libraries provided by Nerfstudio. As an example use case we outline a hardware setup that can be used NerfBridge to train a NeRF from images captured by a camera mounted to a quadrotor in both indoor and outdoor environments. For accompanying video https://youtu.be/EH0SLn-RcDg and code https://github.com/javieryu/nerf_bridge.

翻译：本工作发表于IEEE 2023年国际机器人与自动化大会非常规空间表示研讨会。神经辐射场（NeRF）是一类从彩色图像中建模三维环境的隐式场景表示方法。NeRF具有高表达能力，可建模真实世界中复杂多尺度的几何结构，因此有望成为机器人应用的强大工具。现代NeRF训练库能在数秒内从静态数据集生成逼真的NeRF，但这些库专为离线使用设计，且需要缓慢的位姿优化预计算步骤。本文提出NerfBridge——连接机器人操作系统（ROS）与流行库Nerfstudio的开源桥梁，实现从图像流中实时在线训练NeRF。NerfBridge通过提供可扩展接口，对接Nerfstudio的高效训练流水线与模型库，从而推动NeRF在机器人领域应用研究的快速发展。作为示例应用，我们概述了一种硬件装置：通过搭载于四旋翼飞行器的摄像头采集室内外场景图像，并利用NerfBridge训练NeRF模型。相关视频见https://youtu.be/EH0SLn-RcDg，代码见https://github.com/javieryu/nerf_bridge。