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

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...

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Bibliographic Details
Main Authors: Nunes, Urbano Miguel, Demiris, Yiannis
Format: Conference Proceeding
Language:English
Subjects:
Computer vision
Euclidean distance
Kinematics
Motion segmentation
Three-dimensional displays
Tracking
Trajectory
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Summary: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.
ISSN:2380-7504
DOI:10.1109/ICCV.2019.00391