Plug-And-Play Shape Matching Module for Zero-Shot Mesh-Free Grasp Refinement on Unknown Objects

Abstract

Reliably grasping unknown objects in logistics automation remains a major challenge. While most approaches rely on 3D CAD models or large-scale training, their applicability to novel items is limited. This paper proposes a plug-and-play geometric refinement module that can be appended to any existing grasp planner. The module operates in a training-free and mesh-free manner, estimating an object's approximate centroid from a single RGB-D image to enhance grasp stability. Its core mechanism involves using an initial grasp candidate as an automatic prompt for segmentation, followed by geometric primitive fitting to the isolated object's point cloud. By rescoring grasp candidates based on proximity to the estimated centroid, our module improves physical stability. Experimental results demonstrate that our module improves the success rate of baseline grasp planners by up to 25%p enhancing real-world pick-and-place performance without requiring any offline training or prior object models.