# NerfBridge: Bringing Real-time, Online Neural Radiance Field Training to Robotics

arXiv: https://arxiv.org/abs/2305.09761 · HTML: https://mlsanity.com/paper/2305.09761/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2023.

## Built on by (most cited first, 9 of 9)

- [Splat-MOVER: Multi-Stage, Open-Vocabulary Robotic Manipulation via Editable Gaussian Splatting](https://mlsanity.com/paper/2405.04378/) (2024)
- [Neural Visibility Field for Uncertainty-Driven Active Mapping](https://mlsanity.com/paper/2406.06948/) (2024)
- [NeRFs in Robotics: A Survey](https://mlsanity.com/paper/2405.01333/) (2024)
- [A ROS 2 Wrapper for Florence-2: Multi-Mode Local Vision-Language Inference for Robotic Systems](https://mlsanity.com/paper/2604.01179/) (2026)
- [DISORF: A Distributed Online 3D Reconstruction Framework for Mobile Robots](https://mlsanity.com/paper/2403.00228/) (2024)
- [Active Scout: Multi-Target Tracking Using Neural Radiance Fields in Dense Urban Environments](https://mlsanity.com/paper/2406.07431/) (2024)
- [Timeliness-Fidelity Tradeoff in 3D Scene Representations](https://mlsanity.com/paper/2407.16575/) (2024)
- [Adaptive Framework for Ambient Intelligence in Rehabilitation Assistance](https://mlsanity.com/paper/2507.08624/) (2025)
- [Task-Oriented Communications for 3D Scene Representation: Balancing Timeliness and Fidelity](https://mlsanity.com/paper/2509.17282/) (2025)

## Abstract (arXiv)

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.