用扩散模型极低带宽实现协作感知，性能不降反升。
DiffCP: Ultra-Low Bit Collaborative Perception via Diffusion Model
- 用专用扩散模型压缩感知数据，实现特征级协作
- 通信开销降低14.5倍，性能与顶尖算法持平
- 可无缝嵌入现有系统，适合边缘协作场景
协作感知（CP）正成为克服独立智能局限性的有力方案。然而，当前无线通信系统因带宽需求过大，难以支持特征级和原始级的协作算法。本文提出DiffCP，一种新型的协作感知范式，利用专用扩散模型高效压缩协作方的感知信息。通过将几何与语义条件融入生成模型，DiffCP实现了特征级协作，且通信成本极低，推动了CP系统的实用化。该范式可无缝集成至现有CP算法中，提升多种下游任务表现。大量实验研究了通信、计算与性能间的权衡。数值结果表明，DiffCP可将通信成本降低14.5倍，同时保持与最先进算法相当的性能。
原文摘要 · Abstract (English)
Collaborative perception (CP) is emerging as a promising solution to the inherent limitations of stand-alone intelligence. However, current wireless communication systems are unable to support feature-level and raw-level collaborative algorithms due to their enormous bandwidth demands. In this paper, we propose DiffCP, a novel CP paradigm that utilizes a specialized diffusion model to efficiently compress the sensing information of collaborators. By incorporating both geometric and semantic conditions into the generative model, DiffCP enables feature-level collaboration with an ultra-low communication cost, advancing the practical implementation of CP systems. This paradigm can be seamlessly integrated into existing CP algorithms to enhance a wide range of downstream tasks. Through extensive experimentation, we investigate the trade-offs between communication, computation, and performance. Numerical results demonstrate that DiffCP can significantly reduce communication costs by 14.5-fold while maintaining the same performance as the state-of-the-art algorithm.
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