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[ LS18] Manifold Learning in Quotient Spaces
Conférence Internationale avec comité de lecture : IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018, Salt Lake City, USA,Mots clés: Deep Learning, 3D Unsupervised Learning, Quotient Auto-Encoder
Résumé:
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 autoen-
coder model for encoding and synthesis of 3D shapes. To
get rid of undesirable input variability our model learns a
manifold in a quotient space of the input space. Typically,
we propose to quotient the space of 3D models by the ac-
tion of rotations. Thus, our quotient autoencoder allows to
directly learn in the space of interest, ignoring side infor-
mation. This is reflected in better performances on recon-
struction and interpolation tasks, as our experiments show
that our model outperforms a vanilla autoencoder on the
well-known Shapenet dataset. Moreover, our model learns
a rotation-invariant representation, leading to interesting
results in shapes co-alignment. Finally, we extend our quo-
tient autoencoder to quotient by non-rigid transformations.
BibTeX
| @inproceedings { | |||
| LS18, | |||
| title | = | "{Manifold Learning in Quotient Spaces}", | |
| author | = | " E. Mehr and A. Leutier and F. Sanchez and N. Thome and M. Cord ", | |
| booktitle | = | "{IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}", | |
| year | = | 2018, | |
| month | = | "June", | |
| address | = | "Salt Lake City, USA", | |
| } | |||
