English(EN) GenCOPE: Syn2Real Generalized Category-Level Object Pose Estimation for Robotic Picking

GenCOPE 实现机器人物体姿态的合成到真实估计

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-02 04:00

研究人员开发了 GenCOPE，一种新颖的类别级物体姿态估计（COPE）方法，实现了机器人抓取的合成到真实（Syn2Real）泛化。该方法仅使用合成数据进行训练，克服了模拟环境和真实环境之间，尤其是在纹理外观上的显著域间隙。GenCOPE 采用 2D 和 3D 语义一致性约束来学习域不变表示，并采用端到端的姿态回归框架，通过跨模态融合进行精细估计。该模型轻量级、仅全局特征的架构在 REAL275 和 Wild6D 等基准测试以及真实机器人操作场景中表现出卓越的 Syn2Real 泛化能力。 AI

影响这项研究通过使在模拟中训练的模型能够在真实环境中准确执行，从而显著改进机器人操作，减少了对大量真实世界数据收集的需求。

排序理由这是一篇详细介绍物体姿态估计新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Jian Liu, Wei Sun, Zhenqi Dai, Hui Yang, Jian Xiao, Nicu Sebe, Na Zhao · 2026-10-02 04:00

GenCOPE：用于机器人抓取的 Syn2Real 广义类别级物体姿态估计

arXiv:2610.01758v1 Announce Type: new Abstract: Category-level object pose estimation (COPE), capable of generalizing to intra-class unknown objects, has become a core technique for robotic 3D scene understanding. However, existing COPE methods still require labor-intensive recol…

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GenCOPE：用于机器人抓取的 Syn2Real 广义类别级物体姿态估计

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