When a toddler is presented a new toy, their instinctual behaviour is to pick it up and inspect it with their hand and eyes in tandem, clearly searching over its surface to properly understand what they are playing with. Here, touch provides high fidelity localized information while vision provides complementary global context. However, in 3D shape reconstruction, the complementary fusion of visual and haptic modalities remains largely unexplored. In this paper, we study this problem and present an effective chart-based approach to fusing vision and touch, which leverages advances in graph convolutional networks. To do so, we introduce a dataset of simulated touch and vision signals from the interaction between a robotic hand and a large array of 3D objects. Our results show that (1) leveraging both vision and touch signals consistently improves single-modality baselines; (2) our approach outperforms alternative modality fusion methods and strongly benefits from the proposed chart-based structure; (3) the reconstruction quality increases with the number of grasps provided; and (4) the touch information not only enhances the reconstruction at the touch site but also extrapolates to its local neighborhood.

本文研究3D形状重建中视觉和触觉信息的多模式融合问题，并提出了一种基于图表的方法，该方法有助于提高重建质量，并且通过一个机器人手与3D物体的交互生成了一个仿真数据集。结果表明，利用视觉与触觉信号可以显著改善基准单一模态的重建表现，本方法优于替代模态融合方法并受益于所提出的基于图表的结构。


视觉和触觉的三维形状重建