DiffCP: Ultra-Low Bit Collaborative Perception via Diffusion Model

TL;DR

DiffCP introduces a diffusion model-based approach for collaborative perception that drastically reduces communication bandwidth while maintaining high performance, enabling practical feature-level collaboration.

Contribution

The paper presents a novel diffusion model-based framework for ultra-low bandwidth feature-level collaborative perception, improving efficiency over existing methods.

Findings

Reduces communication cost by 14.5 times

Maintains state-of-the-art performance

Enables practical implementation of CP systems

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.