SparseCoop: Cooperative Perception with Kinematic-Grounded Queries

• URL: http://arxiv.org/abs/2512.06838v1
• Date: Sun, 07 Dec 2025 13:22:06 GMT
• Title: SparseCoop: Cooperative Perception with Kinematic-Grounded Queries
• Authors: Jiahao Wang, Zhongwei Jiang, Wenchao Sun, Jiaru Zhong, Haibao Yu, Yuner Zhang, Chenyang Lu, Chuang Zhang, Lei He, Shaobing Xu, Jianqiang Wang,
• Abstract summary: We propose SparseCoop, a fully sparse cooperative perception framework for 3D detection and tracking.<n> Experiments on2X-Seq and Griffin datasets show SparseCoop achieves state-of-the-art performance.
• Score: 24.54324085409114
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cooperative perception is critical for autonomous driving, overcoming the inherent limitations of a single vehicle, such as occlusions and constrained fields-of-view. However, current approaches sharing dense Bird's-Eye-View (BEV) features are constrained by quadratically-scaling communication costs and the lack of flexibility and interpretability for precise alignment across asynchronous or disparate viewpoints. While emerging sparse query-based methods offer an alternative, they often suffer from inadequate geometric representations, suboptimal fusion strategies, and training instability. In this paper, we propose SparseCoop, a fully sparse cooperative perception framework for 3D detection and tracking that completely discards intermediate BEV representations. Our framework features a trio of innovations: a kinematic-grounded instance query that uses an explicit state vector with 3D geometry and velocity for precise spatio-temporal alignment; a coarse-to-fine aggregation module for robust fusion; and a cooperative instance denoising task to accelerate and stabilize training. Experiments on V2X-Seq and Griffin datasets show SparseCoop achieves state-of-the-art performance. Notably, it delivers this with superior computational efficiency, low transmission cost, and strong robustness to communication latency. Code is available at https://github.com/wang-jh18-SVM/SparseCoop.

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