Federated Learning for Large-Scale Scene Modeling with Neural Radiance Fields

Understand

We envision a system to continuously build and maintain a map based on earth-scale neural radiance fields (NeRF) using data collected from vehicles and drones in a lifelong learning manner.

• However, existing large-scale modeling by NeRF has problems in terms of scalability and maintainability when modeling earth-scale environments.
• Therefore, to address these problems, we propose a federated learning pipeline for large-scale modeling with NeRF.
• We tailor the model aggregation pipeline in federated learning for NeRF, thereby allowing local updates of NeRF.