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While 3D object representations are being revived in the context of multi-view object class detection and scene understanding, they have not yet attained wide-spread use in fine-grained categorization. State-of-the-art approaches achieve remarkable performance when training data is plentiful, but they are typically tied to flat, 2D representations that model objects as a collection of unconnected views, limiting their ability to generalize across viewpoints. In this paper, we therefore lift twodoi:10.1109/iccvw.2013.77 dblp:conf/iccvw/Krause0DF13 fatcat:ybpy5geivnbnrhxyq43xw45f2e