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ShanghaiTech University Knowledge Management System
Federated Neural Radiance Field for Distributed Intelligence | |
2024-06-15 | |
状态 | 已发表 |
摘要 | 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. |
关键词 | federated learning edge intelligence FedNeRF |
DOI | arXiv:2406.10474 |
相关网址 | 查看原文 |
出处 | Arxiv |
WOS记录号 | PPRN:89347880 |
WOS类目 | Computer Science, Hardware& Architecture |
文献类型 | 预印本 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/395936 |
专题 | 信息科学与技术学院 信息科学与技术学院_PI研究组_邵子瑜组 信息科学与技术学院_硕士生 |
通讯作者 | Zhang, Yintian |
作者单位 | ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China |
推荐引用方式 GB/T 7714 | Zhang, Yintian,Shao, Ziyu. Federated Neural Radiance Field for Distributed Intelligence. 2024. |
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