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English(EN) Artic-O: End-to-End Articulated Object Reconstruction via Latent Geometry Learning

Artic-O框架实现高效可动对象重建

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了Artic-O，一种用于从稀疏图像重建可动对象的新型端到端框架。该方法将几何重建、部件推理和关节估计整合到一个单一、高效的过程中。通过将观测映射到潜在几何空间并利用流匹配解码器，Artic-O可以恢复完整的形状（包括被遮挡的结构），并预测可移动部件和运动参数。该系统在效率方面表现出显著的改进，将推理时间从几分钟缩短到几秒钟，同时在PartNet-Mobility数据集上保持或提高了重建质量和关节精度。 AI

影响 这项研究通过实现更高效、更准确的复杂可动对象的3D重建，推动了计算机视觉的发展，可能对机器人和增强现实产生影响。

排序理由 详细介绍3D对象重建新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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Artic-O框架实现高效可动对象重建

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详细介绍3D对象重建新方法的学术论文。[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) · Xuyang Wang, Zhenyu Li, Jian Ding, Habib Slim, Peter Wonka, Hongdong Li, Mohamed Elhoseiny ·

    Artic-O：通过潜在几何学习实现端到端可动对象重建

    arXiv:2606.21938v2 Announce Type: replace Abstract: Reconstructing articulated objects from sparse images requires recovering complete geometry, movable parts, and motion parameters. Recent methods typically separate geometry reconstruction, part reasoning, and articulation estim…

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