  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. In the aggregation step, the accuracy of the
clients' global pose is critical. Thus, we also propose global pose alignment
to align the noisy global pose of clients before the aggregation step. In
experiments, we show the effectiveness of the proposed pose alignment and the
federated learning pipeline on the large-scale scene dataset, Mill19.
