TopoMA：基于分布式推理的拓扑引导多智能体稠密 RGB 三维重建
TopoMA: Topology-Guided Multi-Agent Dense RGB 3D Reconstruction via Distributed Inference
把场景的空间拓扑骨架作为可轻量传输的共享信息，让各智能体只交换拓扑而非稠密地图即可完成跨智能体对齐与子图融合，这一思路可直接迁移到多机器人 SLAM、分布式 NeRF/3DGS 建图等需要低带宽协同的视觉重建场景。
原文依据 each agent can independently store, reconstruct, and incrementally optimize its map while collaborating through lightweight topological information
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论多智能体三维重建作为大规模 VR/AR、机器人集群与数字孪生的关键技术，正受到越来越多的关注。近期的端到端三维重建方法在单智能体场景下取得了强劲性能，但难以直接扩展到多智能体协同场景：在这些场景中，它们往往面临跟踪不稳定、内存消耗过大以及回环检测频繁失败等问题，因而无法满足实时性与大规模部署的需求。为解决这些问题，我们提出 TOPOMA，一个面向多智能体协同的实时端到端三维重建框架。TOPOMA 显式建模场景的空间拓扑结构，并将其与端到端表征学习紧密耦合，从而联合解决智能体间空间对齐与子图融合等核心挑战。具体而言，我们引入了拓扑骨架建模与优化、去中心化回环检测以及拓扑引导的残差传输，并在此基础上构建了全分布式推理架构，其中每个智能体都能独立地存储、重建并增量优化自身地图，同时通过轻量级拓扑信息进行协同。大量实验表明，与现有方法相比，TOPOMA 在轨迹精度、重建质量、鲁棒性与拓扑一致性上均取得了一致的更优表现，展现出优越的适应性与可扩展性。
英文原文摘要 1472 字符
Multi-agent 3D reconstruction, as a key technology for large-scale VR/AR, robot swarms, and digital twins, has attracted growing attention. Recent end-to-end 3D reconstruction methods achieve strong performance in single-agent scenarios, but they are difficult to directly extend to multi-agent collaborative settings, where they often suffer from unstable tracking, excessive memory consumption, and frequent loop-closure failures, thus failing to meet real-time and large-scale deployment requirements. To address these issues, we propose TOPOMA, a real-time end-to-end 3D reconstruction framework tailored for multi-agent collaboration. TOPOMA explicitly models the spatial topological structure of the scene and tightly couples it with end-to-end representation learning, thereby jointly solving core challenges such as inter-agent spatial alignment and submap fusion. Concretely, we introduce topology skeleton modeling and optimization, decentralized loop closure, and topology-guided residual transport, and build upon them a fully distributed inference architecture in which each agent can independently store, reconstruct, and incrementally optimize its map while collaborating through lightweight topological information. Extensive experiments demonstrate that, compared with existing methods, TOPOMA achieves consistently higher trajectory accuracy, reconstruction quality, robustness, and topological consistency, showing superior adaptability and scalability.