Title
Unsupervised Articulated Skeleton Extraction From Point Set Sequences Captured by a Single Depth Camera.
Abstract
How to robustly and accurately extract articulated skeletons from point set sequences captured by a single consumer-grade depth camera still remains to be an unresolved challenge to date. To address this issue, we propose a novel, unsupervised approach consisting of three contributions (steps): (i) a non-rigid point set registration algorithm to first build one-to-one point correspondences among the frames of a sequence; (ii) a skeletal structure extraction algorithm to generate a skeleton with reasonable numbers of joints and bones; (iii) a skeleton joints estimation algorithm to achieve accurate joints. At the end, our method can produce a quality articulated skeleton from a single 3D point sequence corrupted with noise and outliers. The experimental results show that our approach soundly outperforms state of the art techniques, in terms of both visual quality and accuracy.
Year
Venue
Field
2018
THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / EIGHTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE
Computer vision,Computer science,Artificial intelligence,Point set,Skeleton (computer programming),Machine learning
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
20
5
Name
Order
Citations
PageRank
Xuequan Lu16417.63
Honghua Chen262.76
Sai Kit Yeung3604.97
Zhigang Deng4136691.38
Wenzhi Chen514128.65