Our paper on unsupervised 3D segmentation of articulated objects is accepted to IEEE RA-L

[2022.05.17]

The following paper is accepted to the IEEE Robotics and Automation Letters (RA-L):

Unsupervised 3D Link Segmentation of Articulated Objects with a Mixture of Coherent Point Drift by JaeGoo Choy, Geonho Cha, and Songhwai Oh

  • Abstract: In this paper, we address the 3D link segmentation problem of articulated objects using multiple point sets with different configurations. We are motivated by the fact that a point set of an object can be aligned to point sets with different configurations by applying rigid transformations to links. Since existing 3D part segmentation datasets are annotated based on the perspective of a human, we propose a novel dataset of articulated objects, which are annotated based on its kinematic models. We define the point set alignment process as a probability density estimation problem and find the optimal decomposition of the point set and deformations using the EM algorithm. In addition, to improve the segmentation performance, we propose a regularization loss designed with a physical prior of decomposition. We evaluate the proposed method on our dataset, demonstrating that the proposed method achieves the state-of-the-art performance compared to baseline methods. Finally, we also propose an effective target manipulating point proposer, which can be applied to collect multiple point sets from an unknown object with different configurations to better solve the 3D link segmentation problem.
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