In this work, we present Fed3DGS, a scalable 3D reconstruction framework
based on 3D Gaussian splatting (3DGS) with federated learning. Existing
city-scale reconstruction methods typically adopt a centralized approach, which
gathers all data in a central server and reconstructs scenes. The approach
hampers scalability because it places a heavy load on the server and demands
extensive data storage when reconstructing scenes on a scale beyond city-scale.
In pursuit of a more scalable 3D reconstruction, we propose a federated
learning framework with 3DGS, which is a decentralized framework and can
potentially use distributed computational resources across millions of clients.
We tailor a distillation-based model update scheme for 3DGS and introduce
appearance modeling for handling non-IID data in the scenario of 3D
reconstruction with federated learning. We simulate our method on several
large-scale benchmarks, and our method demonstrates rendered image quality
comparable to centralized approaches. In addition, we also simulate our method
with data collected in different seasons, demonstrating that our framework can
reflect changes in the scenes and our appearance modeling captures changes due
to seasonal variations.

Fed3DGS 是一个基于 3D 高斯喷洒（3DGS）与联邦学习的可扩展三维重建框架，其采用分散式架构并利用数以百万计的客户端上的分布式计算资源，通过定制的蒸馏模型更新方案和外观建模技术来处理非独立同分布数据，在大规模基准测试中呈现出与集中式方法相当的图像质量，并能捕捉到由季节变化引起的场景变化。