Shape Retrieval of Non-rigid 3D Human Models

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...

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Bibliographic Details
Published inInternational journal of computer vision Vol. 120; no. 2; pp. 169 - 193
Main Authors Pickup, D., Sun, X., Rosin, P. L., Martin, R. R., Cheng, Z., Lian, Z., Aono, M., Hamza, A. Ben, Bronstein, A., Bronstein, M., Bu, S., Castellani, U., Cheng, S., Garro, V., Giachetti, A., Godil, A., Isaia, L., Han, J., Johan, H., Lai, L., Li, B., Li, C., Li, H., Litman, R., Liu, X., Liu, Z., Lu, Y., Sun, L., Tam, G., Tatsuma, A., Ye, J.
Format Journal Article
LanguageEnglish
Published New York Springer US 01.11.2016
Springer
Springer Nature B.V
Subjects
3-D graphics
Algorithms
Artificial Intelligence
Benchmarking
Benchmarks
Computer graphics
Computer Imaging
Computer Science
Computer vision
Datasets
Graphics software
Human performance
Human subjects
Image Processing and Computer Vision
Image retrieval
Pattern Recognition
Pattern Recognition and Graphics
Shape recognition
Software upgrading
Studies
Three dimensional models
Training
Vision
Websites
3D humans
Benchmark
3D shape retrieval
Non-rigid 3D shape retrieval
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Summary: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.
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ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-016-0903-8