Online Unsupervised Learning of the 3D Kinematic Structure of Arbitrary Rigid Bodies

Urbano Miguel Nunes, Yiannis Demiris; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 3809-3817

Abstract


This work addresses the problem of 3D kinematic structure learning of arbitrary articulated rigid bodies from RGB-D data sequences. Typically, this problem is addressed by offline methods that process a batch of frames, assuming that complete point trajectories are available. However, this approach is not feasible when considering scenarios that require continuity and fluidity, for instance, human-robot interaction. In contrast, we propose to tackle this problem in an online unsupervised fashion, by recursively maintaining the metric distance of the scene's 3D structure, while achieving real-time performance. The influence of noise is mitigated by building a similarity measure based on a linear embedding representation and incorporating this representation into the original metric distance. The kinematic structure is then estimated based on a combination of implicit motion and spatial properties. The proposed approach achieves competitive performance both quantitatively and qualitatively in terms of estimation accuracy, even compared to offline methods.

Related Material


[pdf] [supp]
[bibtex]
@InProceedings{Nunes_2019_ICCV,
author = {Nunes, Urbano Miguel and Demiris, Yiannis},
title = {Online Unsupervised Learning of the 3D Kinematic Structure of Arbitrary Rigid Bodies},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}