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EgoRefine framework enhances asynchronous collaborative perception

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Researchers have developed EgoRefine, a novel framework designed to improve asynchronous collaborative perception in connected agents. This system addresses the challenges of temporal delays in cooperative features by using ego-referenced predictive alignment and trajectory-conditioned reliability-aware fusion. The Ego-referenced Predictive Alignment module guides the prediction and refinement of cooperative trajectory fields based on the ego agent's current features. The Trajectory-conditioned Reliability-aware Fusion module then adaptively reweights the ego and cooperative streams by considering trajectory discrepancies and refinement magnitudes before convolutional fusion. Experiments on the V2V4Real and DAIR-V2X-Seq datasets demonstrated EgoRefine's superior performance over existing methods like TraF-Align. AI

IMPACT Enhances collaborative perception for autonomous systems by improving data fusion under asynchronous communication.

RANK_REASON The cluster describes a new academic paper detailing a novel framework for a computer vision task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

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EgoRefine framework enhances asynchronous collaborative perception

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The cluster describes a new academic paper detailing a novel framework for a computer vision task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Lingzhao Kong, Yongsheng Zang, Yu Kang, Kailun Yang, Jie Fu, Yukun Zuo, Zhiyong Li ·

    EgoRefine: Ego-Referenced Predictive Alignment and Trajectory-Conditioned Reliability-Aware Fusion for Asynchronous Collaborative Perception

    arXiv:2610.00319v1 Announce Type: new Abstract: Collaborative perception enables connected agents to share complementary observations for 3D object detection, extending sensing range and mitigating occlusion. Under asynchronous communication, however, cooperative features arrive …

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