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English(EN) RigidFormer: Learning Rigid Dynamics using Transformers

RigidFormer Transformer 学习无网格刚体动力学

研究人员开发了 RigidFormer,这是一种新颖的基于 Transformer 的模型,旨在从无网格表示中学习刚体动力学。这种以对象为中心的方法利用紧凑的锚点和锚点-顶点池化来整合局部顶点特征,从而能够高效地模拟富含接触的场景。与基于网格的方法相比,RigidFormer 在标准基准测试中表现出更优越的性能和泛化能力,即使输入是点云且分辨率不同,并且可以扩展到数百个对象。 AI

影响 这项研究通过实现对复杂物理交互更高效、更准确的建模,有可能推进机器人和计算机图形学中的仿真能力。

排序理由 该条目是一篇学术论文,详细介绍了一种学习刚体动力学的新模型和方法。 [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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

RigidFormer Transformer 学习无网格刚体动力学

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该条目是一篇学术论文,详细介绍了一种学习刚体动力学的新模型和方法。 [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) · Zhiyang Dou, Minghao Guo, Haixu Wu, Doug Roble, Tuur Stuyck, Wojciech Matusik ·

    RigidFormer:使用Transformer学习刚性动力学

    arXiv:2605.09196v2 Announce Type: replace Abstract: Learning-based simulation of multi-object rigid-body dynamics remains difficult because contact is discontinuous and errors compound over long horizons. Most existing methods remain tied to mesh connectivity and vertex-level mes…