arXiv 2412.176122024-12-23cs.CV

CoSurfGS：面向大规模场景重建的基于分布式学习的协作式3D表面高斯泼溅

CoSurfGS:Collaborative 3D Surface Gaussian Splatting with Distributed Learning for Large Scene Reconstruction

Northwestern Polytechnical University（西北工业大学）；Nanyang Technological University（南洋理工大学）；Zhejiang University（浙江大学）；Shanghai AI Lab（上海人工智能实验室）

Yuanyuan Gao, Yalun Dai, Hao Li, Weicai Ye, Junyi Chen, Danpeng Chen, Dingwen Zhang, Tong He, Guofeng Zhang, Junwei Han

备注：: Our project page is available at \url{https://gyy456.github.io/CoSurfGS}

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AI总结：

提出基于分布式学习的多智能体协作3DGS表面重建框架CoSurfGS，通过局部模型压缩和模型聚合方案解决大规模场景重建的内存与时间瓶颈，实现高保真表面重建与照片级渲染。

摘要：

3D高斯泼溅（3DGS）在场景重建中展现了出色的性能。然而，现有基于GS的表面重建方法多局限于3D物体或有限场景。将其直接应用于大规模场景重建会面临内存开销高、耗时过长及几何细节缺失等挑战，难以在实际中应用。为解决这些问题，我们提出了一种基于分布式学习的多智能体协作快速3DGS表面重建框架，用于大规模表面重建。具体而言，我们开发了局部模型压缩（LMC）和模型聚合方案（MAS），在降低GPU内存消耗的同时实现大场景的高质量表面表示。在Urban3d、MegaNeRF和BlendedMVS上的大量实验表明，该方法能实现快速、可扩展的高保真表面重建和照片级真实感渲染。项目主页见\url{https://gyy456.github.io/CoSurfGS}。

英文摘要：

3D Gaussian Splatting (3DGS) has demonstrated impressive performance in scene reconstruction. However, most existing GS-based surface reconstruction methods focus on 3D objects or limited scenes. Directly applying these methods to large-scale scene reconstruction will pose challenges such as high memory costs, excessive time consumption, and lack of geometric detail, which makes it difficult to implement in practical applications. To address these issues, we propose a multi-agent collaborative fast 3DGS surface reconstruction framework based on distributed learning for large-scale surface reconstruction. Specifically, we develop local model compression (LMC) and model aggregation schemes (MAS) to achieve high-quality surface representation of large scenes while reducing GPU memory consumption. Extensive experiments on Urban3d, MegaNeRF, and BlendedMVS demonstrate that our proposed method can achieve fast and scalable high-fidelity surface reconstruction and photorealistic rendering. Our project page is available at \url{https://gyy456.github.io/CoSurfGS}.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。