mlsanityData to 14 Oct 2026

Cooperative Perception: A Resource-Efficient Framework for Multi-Drone 3D Scene Reconstruction Using Federated Diffusion and NeRF

arXiv 2508.00967 ↗First posted 1 Aug 2025cs.AI cs.ROcited by 0

On the map: Diffusion models → Diffusion and flow policies for robotics

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models. Builds on 57 of its 80 references that are in the map. First posted on arXiv 1 Aug 2025.

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57 of 80 references.

References · 57

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38 more
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  1. 2026FlockDiffusion: Assignment-Conditioned Diffusion for Multi-Drone Task Allocation and Completion ↗
  2. 2026Dense Coverage, Sparse Refinement: Byte-Constrained Cooperative Perception
  3. 2019Learning Vision-based Flight in Drone Swarms by Imitation
  4. 2025AirCopBench: A Benchmark for Multi-drone Collaborative Embodied Perception and Reasoning
  5. 2024Gen-Swarms: Adapting Deep Generative Models to Swarms of Drones
  6. 2026COLMAR: Cooperative View Policy Learning for Multi-Agent Active 3D Reconstruction ↗
  7. 2026AeroScene: Progressive Scene Synthesis for Aerial Robotics
  8. 2025AirVista-II: An Agentic System for Embodied UAVs Toward Dynamic Scene Semantic Understanding