CoCMT: Communication-Efficient Cross-Modal Transformer for Collaborative Perception
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918076603170816 |
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| author | Wang, Rujia Gao, Xiangbo Xiang, Hao Xu, Runsheng Tu, Zhengzhong |
| author_facet | Wang, Rujia Gao, Xiangbo Xiang, Hao Xu, Runsheng Tu, Zhengzhong |
| contents | Multi-agent collaborative perception enhances each agent perceptual capabilities by sharing sensing information to cooperatively perform robot perception tasks. This approach has proven effective in addressing challenges such as sensor deficiencies, occlusions, and long-range perception. However, existing representative collaborative perception systems transmit intermediate feature maps, such as bird-eye view (BEV) representations, which contain a significant amount of non-critical information, leading to high communication bandwidth requirements. To enhance communication efficiency while preserving perception capability, we introduce CoCMT, an object-query-based collaboration framework that optimizes communication bandwidth by selectively extracting and transmitting essential features. Within CoCMT, we introduce the Efficient Query Transformer (EQFormer) to effectively fuse multi-agent object queries and implement a synergistic deep supervision to enhance the positive reinforcement between stages, leading to improved overall performance. Experiments on OPV2V and V2V4Real datasets show CoCMT outperforms state-of-the-art methods while drastically reducing communication needs. On V2V4Real, our model (Top-50 object queries) requires only 0.416 Mb bandwidth, 83 times less than SOTA methods, while improving AP70 by 1.1 percent. This efficiency breakthrough enables practical collaborative perception deployment in bandwidth-constrained environments without sacrificing detection accuracy. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_13504 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CoCMT: Communication-Efficient Cross-Modal Transformer for Collaborative Perception Wang, Rujia Gao, Xiangbo Xiang, Hao Xu, Runsheng Tu, Zhengzhong Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Robotics Multi-agent collaborative perception enhances each agent perceptual capabilities by sharing sensing information to cooperatively perform robot perception tasks. This approach has proven effective in addressing challenges such as sensor deficiencies, occlusions, and long-range perception. However, existing representative collaborative perception systems transmit intermediate feature maps, such as bird-eye view (BEV) representations, which contain a significant amount of non-critical information, leading to high communication bandwidth requirements. To enhance communication efficiency while preserving perception capability, we introduce CoCMT, an object-query-based collaboration framework that optimizes communication bandwidth by selectively extracting and transmitting essential features. Within CoCMT, we introduce the Efficient Query Transformer (EQFormer) to effectively fuse multi-agent object queries and implement a synergistic deep supervision to enhance the positive reinforcement between stages, leading to improved overall performance. Experiments on OPV2V and V2V4Real datasets show CoCMT outperforms state-of-the-art methods while drastically reducing communication needs. On V2V4Real, our model (Top-50 object queries) requires only 0.416 Mb bandwidth, 83 times less than SOTA methods, while improving AP70 by 1.1 percent. This efficiency breakthrough enables practical collaborative perception deployment in bandwidth-constrained environments without sacrificing detection accuracy. |
| title | CoCMT: Communication-Efficient Cross-Modal Transformer for Collaborative Perception |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2503.13504 |