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English(EN) FIND: Frequency Invariance Disentanglement for Test-Time Adaptation in LiDAR 3D Detection

新的FIND框架通过解耦频域来增强激光雷达3D检测

研究人员推出了一种名为FIND(频率不变解耦)的新框架,旨在通过解决域偏移来提高基于激光雷达的3D目标检测(L3OD)性能。FIND在频域中操作,将对域变化不变的内容与特定域的干扰分离开来。该框架使用B样条拟合自适应滤波器和双流架构来提取鲁棒特征并抑制不希望出现的域变化。实验表明,FIND在跨数据集适应和损坏基准测试中的表现显著优于现有方法。 AI

影响 这项研究可能为自动驾驶汽车和机器人领域带来更鲁棒、更准确的3D目标检测系统,尤其是在多变的环境条件下。

排序理由 该集群包含一篇详细介绍计算机视觉任务新技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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新的FIND框架通过解耦频域来增强激光雷达3D检测

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该集群包含一篇详细介绍计算机视觉任务新技术的学术论文。[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) · Dongxiao Li, Zixuan Hu, Ling-Yu Duan ·

    FIND:用于激光雷达3D检测中测试时自适应的频率不变解耦

    arXiv:2611.09170v1 Announce Type: new Abstract: LiDAR-based 3D Object Detection (L3OD) is a fundamental 3D perception task, yet its performance is often compromised by domain shifts arising from diverse sensor configurations and environmental conditions. Existing Test-Time Adapta…