Long-SCOPE: Fully Sparse Long-Range Cooperative 3D Perception
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866915929929023488 |
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| author | Wang, Jiahao Xu, Zikun Zhang, Yuner Jiang, Zhongwei Lu, Chenyang Yang, Shuocheng Wang, Yuxuan Zhong, Jiaru Zhang, Chuang Xu, Shaobing Wang, Jianqiang |
| author_facet | Wang, Jiahao Xu, Zikun Zhang, Yuner Jiang, Zhongwei Lu, Chenyang Yang, Shuocheng Wang, Yuxuan Zhong, Jiaru Zhang, Chuang Xu, Shaobing Wang, Jianqiang |
| contents | Cooperative 3D perception via Vehicle-to-Everything communication is a promising paradigm for enhancing autonomous driving, offering extended sensing horizons and occlusion resolution. However, the practical deployment of existing methods is hindered at long distances by two critical bottlenecks: the quadratic computational scaling of dense BEV representations and the fragility of feature association mechanisms under significant observation and alignment errors. To overcome these limitations, we introduce Long-SCOPE, a fully sparse framework designed for robust long-distance cooperative 3D perception. Our method features two novel components: a Geometry-guided Query Generation module to accurately detect small, distant objects, and a learnable Context-Aware Association module that robustly matches cooperative queries despite severe positional noise. Experiments on the V2X-Seq and Griffin datasets validate that Long-SCOPE achieves state-of-the-art performance, particularly in challenging 100-150 m long-range settings, while maintaining highly competitive computation and communication costs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_09206 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Long-SCOPE: Fully Sparse Long-Range Cooperative 3D Perception Wang, Jiahao Xu, Zikun Zhang, Yuner Jiang, Zhongwei Lu, Chenyang Yang, Shuocheng Wang, Yuxuan Zhong, Jiaru Zhang, Chuang Xu, Shaobing Wang, Jianqiang Computer Vision and Pattern Recognition Cooperative 3D perception via Vehicle-to-Everything communication is a promising paradigm for enhancing autonomous driving, offering extended sensing horizons and occlusion resolution. However, the practical deployment of existing methods is hindered at long distances by two critical bottlenecks: the quadratic computational scaling of dense BEV representations and the fragility of feature association mechanisms under significant observation and alignment errors. To overcome these limitations, we introduce Long-SCOPE, a fully sparse framework designed for robust long-distance cooperative 3D perception. Our method features two novel components: a Geometry-guided Query Generation module to accurately detect small, distant objects, and a learnable Context-Aware Association module that robustly matches cooperative queries despite severe positional noise. Experiments on the V2X-Seq and Griffin datasets validate that Long-SCOPE achieves state-of-the-art performance, particularly in challenging 100-150 m long-range settings, while maintaining highly competitive computation and communication costs. |
| title | Long-SCOPE: Fully Sparse Long-Range Cooperative 3D Perception |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.09206 |