Visual-Tactile Sensing for In-Hand Object Reconstruction
Wenqiang Xu, Zhenjun Yu, Han Xue, Ruolin Ye, Siqiong Yao, Cewu Lu
Abstract
Tactile sensing is one of the modalities humans rely on heavily to perceive the world. Working with vision, this modality refines local geometry structure, measures defor-mation at the contact area, and indicates the hand-object contact state. With the availability of open-source tactile sensors such as DIGIT, research on visual-tactile learning is becoming more accessible and reproducible. Leveraging this tactile sensor, we propose a novel visual-tactile in-hand object reconstruction framework VTacO, and ex-tend it to VTacOH for hand-object reconstruction. Since our method can support both rigid and deformable ob-ject reconstruction, no existing benchmarks are proper for the goal. We propose a simulation environment, VT-Sim, which supports generating hand-object interaction for both rigid and deformable objects. With VT-Sim, we gener-ate a large-scale training dataset and evaluate our method on it. Extensive experiments demonstrate that our pro-posed method can outperform the previous baseline meth-ods qualitatively and quantitatively. Finally, we directly ap-ply our model trained in simulation to various real-world test cases, which display qualitative results. Codes, mod-els, simulation environment, and datasets are available at https://sites.google.com/view/vtaco/.