Paper Info
Title
Shape Retrieval of Non-Rigid 3D Human Models.
Abstract
3D models of humans are commonly used within computer graphics and vision, and so the ability to distinguish between body shapes is an important shape retrieval problem. We extend our recent paper which provided a benchmark for testing non-rigid 3D shape retrieval algorithms on 3D human models. This benchmark provided a far stricter challenge than previous shape benchmarks. We have added 145 new models for use as a separate training set, in order to standardise the training data used and provide a fairer comparison. We have also included experiments with the FAUST dataset of human scans. All participants of the previous benchmark study have taken part in the new tests reported here, many providing updated results using the new data. In addition, further participants have also taken part, and we provide extra analysis of the retrieval results. A total of 25 different shape retrieval methods are compared.
Year
DOI
Venue
2016
10.1007/s11263-016-0903-8
International Journal of Computer Vision
Keywords
DocType
Volume
Benchmark,3D shape retrieval,Non-rigid 3D shape retrieval,3D humans
Journal
120
Issue
ISSN
Citations 
2
0920-5691
3
PageRank 
References 
Authors
0.40
37
28
Name
Order
Citations
PageRank
David Pickup1483.06
Xian-Fang Sun261434.84
Paul L. Rosin32559254.25
Ralph R. Martin43279240.42
Zhi-Quan Cheng545825.06
Zhou-hui Lian647532.27
M. Aono764360.79
Abdessamad Ben Hamza8816.03
Alexander M. Bronstein92978143.17
Michael M. Bronstein104032167.52
S. Bu1130.40
Umberto Castellani1268748.51
S. Cheng1330.40
Valeria Garro14465.24
Andrea Giachetti1530943.39
Afzal Godil1661930.70
Jiecai Han1751.55
Henry Johan1835529.36
Laibin Zhang199515.52
Bin Li2092494.55
Chunyuan Li2146733.86
Haisheng Li22702.90
Roee Litman231629.04
X. Liu2430.40
Z. Liu2530.40
Y. Lu2630.40
A. Tatsuma2718510.86
J. Ye289510.80