Topology-Agnostic Mesh Reconstruction of Deformable Objects from Sparse Touch
Estimating the full shape of a deformable object is especially challenging when vision is unavailable: in the dark, inside an opaque bag, behind the manipulating hand, or under heavy self-occlusion. Touch is the natural sensor in these settings, but touches are sparse and local. We present a single topology-agnostic estimator that reconstructs the full mesh of a deformable object from only a few touches and no vision, using one permutation-invariant cross-attention architecture that handles a 1D rope, a 2D cloth, and a 3D volumetric soft body. The learned estimator reduces reconstruction error by roughly two-thirds relative to non-learned geometric mesh completion and a Gaussian-process s...
| Authors | Everest Yang |
|---|---|
| Primary category | cs.RO |
| Published | 15 July 2026 |
| Last updated at source | 15 July 2026 |
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