arXiv cs.AI· Takuhiro Kaneko·· 11 小时前AI 评分31
通过假设空间推理实现碰撞结构重建（Structure from Collision）
Structure from Collision via Hypothesis-Space Reasoning
AI 导读
研究将 Structure from Collision（SfC）重构为假设空间推理，把隐藏空腔表示为显式结构假设，而非依赖隐式密度更新。方法先用仅前向的 MPM 模拟在假设空间做无梯度全局优化，再用可微 MPM 以拉格朗日粒子系统做梯度局部精修。在 SfC benchmark 上，该方法在隐藏结构重建与未来预测上优于基于密度的 SfC 基线，论文被 WACV 2027 接收。
正文 · 原文
Abstract:Knowledge of the hidden interior structure of an object, including the presence and location of cavities, is essential for predictive simulation, robotic manipulation, and nondestructive inspection. However, such cavities leave no direct signature on the static visible surface, making collision-induced deformation one of the few cues used to infer their presence. Structure from Collision (SfC) addresses this problem by recovering the hidden interior structure of an object from multiview observations of a collision event. SfC is challenging for two reasons: hidden cavities are only indirectly observable through surface motion, and voids correspond to the absence of material, making direct optimization using standard density representations difficult. The density-based SfC baseline inherits both difficulties. Surface-matching gradients do not directly indicate the cavity location, while density gradients can be weak or uninformative inside or near void regions. Therefore, we reformulate SfC as hypothesis-space reasoning, representing hidden cavities as explicit structural hypotheses whose variables encode available structural assumptions rather than relying on implicit density updates. A global stage performs gradient-free optimization over this hypothesis space using forward-only material point method (MPM) simulation. A local stage then refines the selected hypothesis as a Lagrangian particle system through gradient-based optimization using differentiable MPM simulation. On the SfC benchmark, our method improves hidden-structure reconstruction and future prediction over the density-based SfC baseline. Interior-topology stress tests and robustness analyses characterize its scope and limitations. The project page is available at this https URL.
| Comments: | Accepted to WACV 2027. Project page: this https URL |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Graphics (cs.GR); Machine Learning (cs.LG); Robotics (cs.RO) |
| Cite as: | arXiv:2611.07900 [cs.CV] |
| (or arXiv:2611.07900v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2611.07900 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Takuhiro Kaneko [view email]
[v1]
Thu, 5 Nov 2026 12:20:11 UTC (27,174 KB)
来源：arXiv cs.AI · arxiv.org