Pickup, D.Sun, X.Rosin, P. L.Martin, R. R.Cheng, Z.Lian, Z.Aono, M.Hamza, A. BenBronstein, A.Bronstein, M.Bu, S.Castellani, U.Cheng, S.Garro, V.Giachetti, A.Godil, A.Han, J.Johan, H.Lai, L.Li, B.Li, C.Li, H.Litman, R.Liu, X.Liu, Z.Lu, Y.Tatsuma, A.J.Ye,Benjamin Bustos and Hedi Tabia and Jean-Philippe Vandeborre and Remco Veltkamp2014-12-152014-12-152014978-3-905674-58-31997-0463https://doi.org/10.2312/3dor.20141056https://diglib.eg.org/handle/10.2312/3dor.20141056.101-110We have created a new benchmarking dataset for testing non-rigid 3D shape retrieval algorithms, one that is much more challenging than existing datasets. Our dataset features exclusively human models, in a variety of body shapes and poses. 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. In this track nine groups have submitted the results of a total of 22 different methods which have been tested on our new dataset.