S4R：刚体穿透消除的缩放方法
S4R: Scaling for Rigid-Body Interpenetration Resolution
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中文总结 AI 辅助
S4R提出一种尺度延续方法，通过均匀缩放和凸接触QP序列，高效解决大规模刚体场景的静态穿透问题，实现零穿透且位移小、耗时低。
中文摘要 AI 辅助
在程序化组装和生成的场景中，刚体之间的相互穿透频繁发生，必须在物理模拟等下游应用之前予以消除。我们提出了S4R（Scaling for Rigid-Body Interpenetration Resolution，用于刚体穿透消除的缩放方法），一种用于静态穿透修复的尺度延续方法。S4R首先将每个物体围绕一个固定的参考中心均匀缩小到一个较小的初始尺度，在该尺度下布局无穿透，然后通过一系列最小范数凸接触二次规划（QP）恢复完整尺度，这些QP在延续过程中针对线性化的分离余量进行优化。因此，解析过程用一系列浅接触子问题替代了一次深度修正。保守的尺度事件界限和冻结见证间隙预测减少了精确网格查询的数量；延续过程以全尺度评估器检查和有界尾部细化结束。我们在Kubric、HY3D-Bench和Thingi10K上使用共享的网格级评估器和统一的逐场景计时协议评估了S4R。在所有三个基准的主要比较中，对于多达N=5000个物体，S4R达到了零报告的穿透，位移保持较小且几乎与场景大小无关，并且在每个硬件层级中，与所比较的方法相比，墙钟时间最低。GPU实现将这些结果扩展到大规模场景。我们的代码和数据可在项目页面找到：此https URL。
英文摘要
Rigid-body interpenetration frequently occurs in procedurally assembled and generated scenes and must be removed before downstream applications such as physical simulation. We present S4R (Scaling for Rigid-Body Interpenetration Resolution), a scale-continuation method for static interpenetration repair. S4R first uniformly shrinks each body about a fixed reference center to a small initial scale, at which the layout is penetration-free, and then restores full scale through a sequence of minimum-norm convex contact quadratic programs (QPs) that target the linearized separation margin during continuation. Resolution thereby replaces one deep correction with a sequence of shallow-contact subproblems. A conservative scale-event bound and frozen-witness gap predictions cut the number of exact mesh queries; the continuation then ends with a full-scale evaluator check and bounded tail refinement. We evaluate S4R on Kubric, HY3D-Bench, and Thingi10K using a shared mesh-level evaluator and a unified per-scene timing protocol. In the main comparisons on all three benchmarks, up to N=5000 bodies, S4R reaches zero reported penetration with displacement that stays small and nearly independent of scene size, and at the lowest wall time within each hardware tier among the compared methods. A GPU implementation extends these results to large-scale scenes. Our code and data can be found on our project page: https://frank-zy-dou.github.io/projects/S4R/index.html.
发表机构
- MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）
- Xiamen University（厦门大学）
- The University of Hong Kong（香港大学）
- Macau University of Science and Technology（澳门科技大学）
- The Hong Kong University of Science and Technology（香港科技大学）
- The University of Texas at Dallas（德克萨斯大学达拉斯分校）
机构由 AI 辅助整理，请以论文原文为准。
