NTT 提出 Structure from Collision：从碰撞变形推断物体内部空腔结构
Structure from Collision via Hypothesis-Space Reasoning
NTT 的团队做了个有意思的研究：光看物体碰撞时的表面形变，就能推断里面有没有空腔、在哪。对做机器人和无损检测的人很实用。
来自 NTK 等研究团队的论文提出 Structure from Collision（SfC）方法，通过多视角观测碰撞事件来恢复物体隐藏的内部结构，如空腔的位置。由于空腔在静态表面没有直接痕迹，且对应材料的缺失，标准密度表征的直接优化难以奏效。该方法将问题重构为假设空间推理：先用无梯度优化和前向 MPM 模拟在假设空间中搜索，再用可微 MPM 模拟对选定假设做基于梯度的局部细化。在 SfC 基准上，该方法在隐藏结构重建和未来预测两方面优于基于密度的 SfC 基线。
Structure from Collision via Hypothesis-Space Reasoning
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 https://www.kecl.ntt.co.jp/people/kaneko.takuhiro/projects/sfc-hypothesis/.