Shape Retrieval of Non-rigid 3D Human Models

• Published in: International journal of computer vision 2016-11, Vol.120 (2), p.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: Article
• Language: English
• 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
• ISSN: 0920-5691; 1573-1405
• DOI: 10.1007/s11263-016-0903-8

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.