Unsupervised 3D Link Segmentation of Articulated Objects with a Mixture of Coherent Point Drift

Jae Goo Choy, Geonho Cha, Songhwai Oh

Abstract:

In this letter, 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 do not provide motion-based annotations,
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.