The ability of neural radiance fields or NeRFs to conduct accurate 3D modelling has motivated application of the technique to scene representation. Previous approaches have mainly followed a centralised learning paradigm, which assumes that all training images are available on one compute node for training. In this paper, we consider training NeRFs in a federated manner, whereby multiple compute nodes, each having acquired a distinct set of observations of the overall scene, learn a common NeRF in parallel. This supports the scenario of cooperatively modelling a scene using multiple agents. Our contribution is the first federated learning algorithm for NeRF, which splits the training effort across multiple compute nodes and obviates the need to pool the images at a central node. A technique based on low-rank decomposition of NeRF layers is introduced to reduce bandwidth consumption to transmit the model parameters for aggregation. Transferring compressed models instead of the raw data also contributes to the privacy of the data collecting agents.

翻译：神经辐射场（NeRF）具备精确三维建模的能力，促使其技术被广泛应用于场景表示。现有研究主要遵循集中式学习范式，即所有训练图像需集中存储于单一计算节点进行训练。本文提出以联邦方式训练NeRF，即多个计算节点各自获取整体场景的不同观测数据后，并行学习共享的NeRF模型。这种方案支持通过多智能体协同完成场景建模。我们的核心贡献在于提出了首个针对NeRF的联邦学习算法，该算法将训练任务分散至多个计算节点，无需在中央节点汇聚图像数据。通过引入基于NeRF层低秩分解的技术，有效降低了模型参数聚合时的带宽消耗。传输压缩模型而非原始数据，亦有助于保护数据采集智能体的隐私。