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.
@article{arxiv.2409.19592,
title = {DiffCP: Ultra-Low Bit Collaborative Perception via Diffusion Model},
author = {Ruiqing Mao and Haotian Wu and Yukuan Jia and Zhaojun Nan and Yuxuan Sun and Sheng Zhou and Deniz Gündüz and Zhisheng Niu},
journal= {arXiv preprint arXiv:2409.19592},
year = {2024}
}