Novel view synthesis (NVS) is an important technology for many AR and VR applications. The recently proposed Neural Radiance Field (NeRF) approach has demonstrated superior performance on NVS tasks, and has been applied to other related fields. However, certain application scenarios with distributed data storage may pose challenges on acquiring training images for the NeRF approach, due to strict regulations and privacy concerns. In order to overcome this challenge, we focus on FedNeRF, a federated learning (FL) based NeRF approach that utilizes images available at different data owners while preserving data privacy. In this paper, we first construct a resource-rich and functionally diverse federated learning testbed. Then, we deploy FedNeRF algorithm in such a practical FL system, and conduct FedNeRF experiments with partial client selection. It is expected that the studies of the FedNeRF approach presented in this paper will be helpful to facilitate future applications of NeRF approach in distributed data storage scenarios.

翻译：新视角合成（NVS）是增强现实与虚拟现实应用中的关键技术。近期提出的神经辐射场（NeRF）方法在NVS任务中展现出卓越性能，并已拓展至相关领域。然而，在分布式数据存储的应用场景中，由于严格的监管要求与隐私考量，获取NeRF训练图像面临挑战。为应对此问题，本文聚焦于FedNeRF——一种基于联邦学习（FL）的NeRF方法，该方法能够利用不同数据所有者持有的图像资源同时保障数据隐私。本文首先构建了资源丰富且功能多样的联邦学习测试平台，随后在实际FL系统中部署FedNeRF算法，并开展基于部分客户端选择的FedNeRF实验。本研究对FedNeRF方法的探索，有望推动NeRF技术在分布式数据存储场景中的未来应用。