A Hand Motion-guided Articulation and Segmentation Estimation
This work addresses the challenge of object manipulation and understanding in robotics or computer vision, but it appears incremental as it builds on existing methods like ICP.
The paper tackles the problem of estimating articulation models and segmenting articulated objects in RGB-D images by leveraging human hand motion, achieving robust performance across various objects.
In this paper, we present a method for simultaneous articulation model estimation and segmentation of an articulated object in RGB-D images using human hand motion. Our method uses the hand motion in the processes of the initial articulation model estimation, ICP-based model parameter optimization, and region selection of the target object. The hand motion gives an initial guess of the articulation model: prismatic or revolute joint. The method estimates the joint parameters by aligning the RGB-D images with the constraint of the hand motion. Finally, the target regions are selected from the cluster regions which move symmetrically along with the articulation model. Our experimental results show the robustness of the proposed method for the various objects.