Touch-HOI: Active Tactile 3D Reconstruction from Hand–Object Interactions

Humans can infer an object's shape through touch, even when hidden inside an opaque container. For robots, tactile 3D reconstruction without visual input remains challenging because sparse contacts reveal only local surface patches. Existing methods underexploit structured hand–object interaction cues and lack explicit posterior representations to reason about shape uncertainty. In this paper, we present Touch-HOI, a unified generative framework for tactile 3D reconstruction with active exploration. Specifically, we formally unify tactile sensing and proprioception as Hand-Object Interaction Evidence, and encode their spatial distribution and statistics as Hand-Object Interaction Encoding. Building upon this, we construct a flow matching model with a pretrained 3D prior, establishing an explicit generative belief state over several shape hypotheses. Therefore, belief-guided grasp selection evaluates prospective tactile feedback via Bayesian reweighting to maximize expected geometric risk reduction without training an extra exploration policy. Extensive experiments on the ABC dataset demonstrate that Touch-HOI achieves mean Chamfer Distances of $2.399$ with five random grasps and $2.093$ with active selection, which verifies the effectiveness of the proposed Touch-HOI. The code will be made publicly available upon acceptance.