Federated Neural Radiance Field for Distributed Intelligence
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866917695043141632 |
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| author | Zhang, Yintian Shao, Ziyu |
| author_facet | Zhang, Yintian Shao, Ziyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_10474 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Federated Neural Radiance Field for Distributed Intelligence Zhang, Yintian Shao, Ziyu Distributed, Parallel, and Cluster Computing 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. |
| title | Federated Neural Radiance Field for Distributed Intelligence |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2406.10474 |