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.

翻译：我们设想了一种系统，能够基于地球尺度的神经辐射场（NeRF），利用从车辆和无人机收集的数据，以终身学习的方式持续构建和维护地图。然而，现有基于NeRF的大规模建模在处理地球尺度环境时存在可扩展性和可维护性问题。为此，我们提出了一种面向NeRF大规模建模的联邦学习流水线。我们针对NeRF定制了联邦学习中的模型聚合流水线，从而支持NeRF的本地更新。在聚合步骤中，客户端全局位姿的准确性至关重要。因此，我们还提出了全局位姿对齐方法，以在聚合步骤前对齐客户端存在噪声的全局位姿。实验表明，所提出的位姿对齐方法及联邦学习流水线在大规模场景数据集Mill19上具有有效性。