A Hand Motion-guided Articulation and Segmentation Estimation

By Richard Sahala Hartanto, Ryoichi Ishikawa, Menandro Roxas, Takeshi Oishi. 2020

4 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, 3D Vision & Pose, Human-Computer Interaction

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

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In Human-Computer Interaction

Citations in the corpus

Habitat 2.0: Training Home Assistants to Rearrange their Habitat (2021, background): “Crafting an SPA pipeline for opening/closing unknown articulated containers is an open unsolved problem in robotics – involving detecting and tracking articulation [70, 71] without models, constrained full-body planning [72– 74] without hand engineering constraints, and designing controllers to hand”