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English(EN) Symmetry-Aware 9D Pose Estimation with Sim(3)-Consistent Feature and Spherical Inception Convolution

新方法利用LVM增强9D物体姿态估计

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新颖的类别级物体姿态估计方法，解决了泛化到未见物体能力的局限性。该方法利用了语义引导的对称感知模块，利用大型视觉模型（LVM）在不需要形状先验的情况下准确推断平移和大小。此外，特征融合模块使用球形卷积将LVM语义特征与几何特征相结合，以有效地建模长距离依赖关系。该方法在基准测试中取得了最先进的成果，并已应用于开发机器人抓取系统。 AI

影响 通过提高物体姿态估计的准确性和泛化能力，增强了机器人的感知和操作能力。

排序理由 该集群包含一篇详细介绍物体姿态估计新方法的论文。

在 arXiv cs.CV 阅读 →

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新方法利用LVM增强9D物体姿态估计

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该集群包含一篇详细介绍物体姿态估计新方法的论文。
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Panfei Cheng, Hongshan Yu, Wenrui Chen, Xiaojun Tang, Jian Liu, Naveed Akhtar ·

    具有 Sim(3) 一致性特征和球形inception卷积的对称感知九维姿态估计

    arXiv:2606.02219v1 Announce Type: new Abstract: Object pose estimation is a fundamental problem for an agent system to perceive or manipulate objects in images or videos. However, current instance-level methods struggle with generalization to unseen objects. Category-level method…

  2. arXiv cs.CV TIER_1 English(EN) · Naveed Akhtar ·

    具有Sim(3)-一致特征和球形卷积的对称感知9D姿态估计

    Object pose estimation is a fundamental problem for an agent system to perceive or manipulate objects in images or videos. However, current instance-level methods struggle with generalization to unseen objects. Category-level methods seek to address this, but remain constrained b…

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