DiffCP: Ultra-Low Bit Collaborative Perception via Diffusion Model
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929520423993344 |
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| author | Mao, Ruiqing Wu, Haotian Jia, Yukuan Nan, Zhaojun Sun, Yuxuan Zhou, Sheng Gündüz, Deniz Niu, Zhisheng |
| author_facet | Mao, Ruiqing Wu, Haotian Jia, Yukuan Nan, Zhaojun Sun, Yuxuan Zhou, Sheng Gündüz, Deniz Niu, Zhisheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_19592 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DiffCP: Ultra-Low Bit Collaborative Perception via Diffusion Model Mao, Ruiqing Wu, Haotian Jia, Yukuan Nan, Zhaojun Sun, Yuxuan Zhou, Sheng Gündüz, Deniz Niu, Zhisheng Computer Vision and Pattern Recognition Machine Learning Multiagent Systems 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. |
| title | DiffCP: Ultra-Low Bit Collaborative Perception via Diffusion Model |
| topic | Computer Vision and Pattern Recognition Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2409.19592 |