通过碰撞视频还原物体内外结构，突破传统方法仅能重建表面的局限
Structure from Collision
- 基于碰撞时外观变化反推隐藏内部结构，结合物理与关键帧约束优化
- 在115个不同形状和材质的物体上验证，可准确恢复腔体位置、大小与形态
- 提出体积渐进搜索策略，避免局部最优，适合需要内部结构建模的研究
近年来，神经3D表示如神经辐射场（NeRF）和3D高斯泼溅（3DGS）已能从多视角图像中精确重建可见外部结构。然而，这类方法难以识别被表面遮挡的内部隐藏结构。为克服此限制，本文提出新任务“碰撞结构重建”（Structure from Collision, SfC），旨在通过物体碰撞过程中的外观变化来估计其完整结构（包括不可见内部结构）。为此，我们提出SfC-NeRF模型，利用视频序列在物理一致性、外观保持及关键帧约束下优化内部结构。为应对问题固有的病态性导致的局部最优问题，我们引入体积渐进策略（volume annealing），通过反复缩小与扩展搜索空间以寻找全局最优解。在包含115个具有多样化腔体形状、位置与尺寸以及材料特性的物体上的大量实验表明，该方法有效揭示了SfC的特性，并验证了SfC-NeRF的优越性能。
原文摘要 · Abstract (English)
Recent advancements in neural 3D representations, such as neural radiance fields (NeRF) and 3D Gaussian splatting (3DGS), have enabled the accurate estimation of 3D structures from multiview images. However, this capability is limited to estimating the visible external structure, and identifying the invisible internal structure hidden behind the surface is difficult. To overcome this limitation, we address a new task called Structure from Collision (SfC), which aims to estimate the structure (including the invisible internal structure) of an object from appearance changes during collision. To solve this problem, we propose a novel model called SfC-NeRF that optimizes the invisible internal structure of an object through a video sequence under physical, appearance (i.e., visible external structure)-preserving, and keyframe constraints. In particular, to avoid falling into undesirable local optima owing to its ill-posed nature, we propose volume annealing; that is, searching for global optima by repeatedly reducing and expanding the volume. Extensive experiments on 115 objects involving diverse structures (i.e., various cavity shapes, locations, and sizes) and material properties revealed the properties of SfC and demonstrated the effectiveness of the proposed SfC-NeRF.
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