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：我们设想一个系统，能够基于地球尺度神经辐射场（NeRF），以终生学习的方式利用车辆和无人机采集的数据持续构建并维护地图。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。因此，为解决这些问题，我们提出了一种用于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程，从而支持NeRF的本地更新。在聚合步骤中，客户端全局位姿的精确性至关重要。为此，我们还提出在聚合步骤之前进行全局位姿对齐，以校准客户端的含噪全局位姿。实验中，我们在大规模场景数据集Mill19上验证了所提位姿对齐方法及联邦学习流水线的有效性。