在关键处优化：面向可变形物体操作的接触自适应网格划分
Refine Where It Matters: Contact-Adaptive Meshing for Deformable Object Manipulation
AI总结：
该研究提出接触自适应网格划分框架，通过GPU并行的局部网格优化提升可变形物体操作的模拟保真度与效率，为接触丰富的模拟应用提供实用基础。
摘要：
可变形物体操作需要准确建模高度局部化的接触，同时保持适合交互使用的模拟速度。均匀精细的四面体网格可以解析这些相互作用，但会在整个物体中引入不必要的自由度；粗网格效率更高，但可能无法充分解析接触诱导的变形。我们提出一种接触自适应网格划分框架，仅在最有用的区域引入空间分辨率。该方法使用可变形体求解器已有的几何、弹性和接触量对候选边进行评分，完全GPU并行的过程随后选择无冲突子集，并在模拟循环内直接更新网格拓扑和物理状态。我们在真实可变形物体操作序列上评估该方法，自适应细化降低了模拟到现实的误差，提高了与精细网格参考的一致性，且比均匀精细模拟效率显著更高。这些结果表明，局部细化可在不产生全局精细离散化成本的情况下提升物理保真度，该框架为交互操作、模拟到现实建模及在线物理数字孪生中的接触丰富模拟提供了实用基础。
英文摘要：
Deformable-object manipulation requires accurate modeling of highly localized contact while maintaining simulation speeds suitable for interactive use. Uniformly fine tetrahedral meshes can resolve these interactions, but they introduce unnecessary degrees of freedom throughout the object. Coarse meshes are more efficient, yet they may under-resolve contact-induced deformation. We present a contact-adaptive mesh refinement framework that introduces spatial resolution only where it is most useful. The method uses geometric, elastic, and contact quantities already available from the deformable-body solver to score candidate edges. A fully GPU-parallel procedure then selects a conflict-free subset and updates both mesh topology and physical state directly within the simulation loop. We evaluate the approach on real deformable-object manipulation sequences. Adaptive refinement reduces simulation-to-real error, improves agreement with a fine-mesh reference, and remains substantially more efficient than uniformly fine simulation. These results show that local refinement can improve physical fidelity without incurring the cost of a globally fine discretization. The framework provides a practical basis for contact-rich simulation in interactive manipulation, sim-to-real modeling, and online physical digital twins.
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