DiffCP: Ultra-Low Bit Collaborative Perception via Diffusion Model

• URL: http://arxiv.org/abs/2409.19592v1
• Date: Sun, 29 Sep 2024 07:19:54 GMT
• Title: DiffCP: Ultra-Low Bit Collaborative Perception via Diffusion Model
• Authors: Ruiqing Mao, Haotian Wu, Yukuan Jia, Zhaojun Nan, Yuxuan Sun, Sheng Zhou, Deniz Gündüz, Zhisheng Niu,
• Abstract summary: Collaborative perception (CP) is emerging as a promising solution to the inherent limitations of stand-alone intelligence. We propose DiffCP, a novel CP paradigm that utilizes a specialized diffusion model to efficiently compress the sensing information of collaborators. We show that DiffCP can significantly reduce communication costs by 14.5-fold while maintaining the same performance as the state-of-the-art algorithm.
• Score: 44.23276350463173
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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