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When learning 3D shapes we are usually interested in their intrinsic geometry rather than in their orientation. To deal with the orientation variations the usual trick consists in augmenting the data to exhibit all possible variability, and thus let the model learn both the geometry as well as the rotations. In this paper we introduce a new autoencoder model for encoding and synthesis of 3D shapes. To get rid of undesirable input variability our model learns a manifold in a quotient space ofdoi:10.1109/cvpr.2018.00955 dblp:conf/cvpr/MehrLBGTC18 fatcat:5ib5i2txtfdfxkljuvjmrlnv64