DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo

Circles represent the contact points in the human demo / grasping points for robot manipulation. Dense 3D correspondence can enhance robotic manipulation by enabling the generalization of spatial, functional, and dynamic information from one object to an unseen counterpart. Compared to shape correspondence, semantic correspondence is more effective in generalizing across different object categories. DenseMatcher first computes vertex features by projecting multiview 2D features onto meshes and refining them with a 3D network, and subsequently finds dense correspondences with the obtained features using functional map. In addition, we craft the first 3D matching dataset that contains colored object meshes across diverse categories. In our experiments, we show that DenseMatcher significantly outperforms prior 3D matching baselines by 43.5%. We demonstrate the downstream effectiveness of DenseMatcher in (i) robotic manipulation, where it achieves crossinstance and cross-category generalization on long-horizon complex manipulation tasks from observing only one demo; (ii) zero-shot color mapping between digital assets, where appearance can be transferred between different objects with relatable geometry. Correspondence plays a pivotal role in robotics Wang (2019). For instance, in robotic assembly, it is necessary to determine the corresponding parts between the target and source objects.