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3D articulated skeleton extraction using a single consumer-grade depth camera

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journal contribution
posted on 2019-11-01, 00:00 authored by Xuequan Lu, Z Deng, J Luo, W Chen, S K Yeung, Y He
Articulated skeleton extraction or learning has been extensively studied for 2D (e.g., images and video) and 3D (e.g., volume sequences, motion capture, and mesh sequences) data. Nevertheless, robustly and accurately learning 3D articulated skeletons from point set sequences captured by a single consumer-grade depth camera still remains challenging, since such data are often corrupted with substantial noise and outliers. Relatively few approaches have been proposed to tackle this problem. In this paper, we present a novel unsupervised framework to address this issue. Specifically, we first build one-to-one point correspondences among the point cloud frames in a sequence with our non-rigid point cloud registration algorithm. We then generate a skeleton involving a reasonable number of joints and bones with our skeletal structure extraction algorithm. We lastly present an iterative Linear Blend Skinning based algorithm for accurate joints learning. At the end, our method can learn a quality articulated skeleton from a single 3D point sequence possibly corrupted with noise and outliers. Through qualitative and quantitative evaluations on both publicly available data and in-house Kinect-captured data, we show that our unsupervised approach soundly outperforms state of the art techniques in terms of both quality (i.e., visual) and accuracy (i.e., Euclidean distance error metric). Moreover, the poses of our extracted skeletons are even comparable to those by KinectSDK, a well-known supervised pose estimation technique; for example, our method and KinectSDK achieves similar distance errors of 0.0497 and 0.0521.

History

Journal

Computer vision and image understanding

Volume

188

Article number

102792

Pagination

1 - 13

Publisher

Elsevier

Location

Amsterdam, The Netherlands

ISSN

1077-3142

eISSN

1090-235X

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2019, Elsevier Inc.