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English(EN) AnyBox: Efficient Zero-Shot 9DoF Pose Estimation of Boxes for Robotic Manipulation

AnyBox框架在箱体姿态估计中实现36个AP点增益

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了AnyBox，一个新颖的零样本框架，旨在对杂乱环境中箱体的9DoF姿态进行精确估计。该系统利用箱体的几何规律性，从单一的RGB-D观测中联合确定其姿态和尺寸。AnyBox包含一个深度一致性滤波器和一个提前停止规则，以处理对称性和遮挡等挑战，显著提高了机器人抓取任务的检测精度和成功率。 AI

影响 该框架通过在杂乱环境中实现更好的物体识别，有望显著提高物流和制造业中机器人抓取任务的效率和准确性。

排序理由 该集群包含一篇详细介绍新姿态估计方法的 ist research paper. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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

AnyBox框架在箱体姿态估计中实现36个AP点增益

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该集群包含一篇详细介绍新姿态估计方法的 ist research paper. [lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yintao Ma, Sajjad Pakdamansavoji, Charles Eret, Rui Heng Yang, Xuan Zhao, Yingxue Zhang, Tongtong Cao, Amir Rasouli ·

    AnyBox：机器人抓取中箱体的零样本高效9自由度姿态估计

    arXiv:2511.15884v2 Announce Type: replace-cross Abstract: Recovering the 9D pose of objects, both their 6D pose and 3D dimensions, under clutter and occlusion is a core requirement for warehouse automation, logistics, and manufacturing. Model-based methods are accurate but assume…

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