多无人机协同重建3D场景，省算力还保隐私。
Cooperative Perception: A Resource-Efficient Framework for Multi-Drone 3D Scene Reconstruction Using Federated Diffusion and NeRF
- 用联邦扩散模型+轻量级目标检测共享语义信息
- 本地更新NeRF并压缩数据，实现高效协同重建
- 适合资源受限的无人机群智能系统
该研究提出一种新型无人机群感知系统，解决计算资源有限、通信带宽低及实时场景重建难题。通过联合训练共享扩散模型与YOLOv12轻量级语义提取，并结合本地NeRF更新与语义感知压缩协议，实现高效的多智能体3D/4D场景合成，兼顾隐私保护与系统可扩展性。框架重新设计生成式扩散模型以支持协同重建，提升协作感知能力。可通过仿真验证，具备在真实无人机测试平台部署潜力，是自主系统多智能体AI的突破性进展。
原文摘要 · Abstract (English)
The proposal introduces an innovative drone swarm perception system that aims to solve problems related to computational limitations and low-bandwidth communication, and real-time scene reconstruction. The framework enables efficient multi-agent 3D/4D scene synthesis through federated learning of shared diffusion model and YOLOv12 lightweight semantic extraction and local NeRF updates while maintaining privacy and scalability. The framework redesigns generative diffusion models for joint scene reconstruction, and improves cooperative scene understanding, while adding semantic-aware compression protocols. The approach can be validated through simulations and potential real-world deployment on drone testbeds, positioning it as a disruptive advancement in multi-agent AI for autonomous systems.
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