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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
DOIarXiv: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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