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

EgoRefine框架增强异步协作感知

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了EgoRefine，一个旨在提高互联代理中异步协作感知能力的新型框架。该系统通过使用自引用预测对齐和轨迹条件可靠性感知融合来解决合作特征中的时间延迟挑战。自引用预测对齐模块基于自代理的当前特征，指导合作轨迹场的预测和细化。然后，轨迹条件可靠性感知融合模块在卷积融合之前，通过考虑轨迹差异和细化幅度，自适应地重新加权自代理和合作数据流。在V2V4Real和DAIR-V2X-Seq数据集上的实验表明，EgoRefine的性能优于TraF-Align等现有方法。 AI

影响 通过改进异步通信下的数据融合，增强了自主系统的协作感知能力。

排序理由 该集群描述了一篇关于计算机视觉任务新框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

EgoRefine框架增强异步协作感知

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该集群描述了一篇关于计算机视觉任务新框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

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

    EgoRefine：用于异步协作感知的自引用预测对齐和轨迹条件可靠性感知融合

    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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