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We introduce a new framework for learning dense correspondence between deformable 3D shapes.
Surface simplification using quadric error metrics
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Dimensionality reduction by learning an invariant mapping
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Blended intrinsic maps
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A survey on shape correspondence
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Microsoft Kinect sensor and its effect
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Sparse modeling of intrinsic correspondences
J. Pokrass, A. M. Bronstein, M. M. Bronstein, P. Sprechmann, and G. Sapiro · 2013
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FAUST: Dataset and evaluation for 3D mesh registration
F. Bogo, J. Romero, M. Loper, and M. J. Black · 2014
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Supervised descriptor learning for non-rigid shape matching
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Functional map networks for analyzing and exploring large shape collections
Q. Huang, F. Wang, and L. J. Guibas · 2014
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Learning spectral descriptors for deformable shape correspondence
R. Litman and A. M. Bronstein · 2014
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Dense non-rigid shape correspondence using random forests
E. Rodolà, S. Rota Bulò, T. Windheuser, M. Vestner, and D. Cremers · 2014
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Optimal intrinsic descriptors for non-rigid shape analysis
T. Windheuser, M. Vestner, E. Rodolà, R. Triebel, and D. Cremers · 2014
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Tensorflow: Large-scale machine learning on heterogeneous systems, 2015
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On the optimality of shape and data representation in the spectral domain
Learning shape correspondence with anisotropic convolutional neural networks
D. Boscaini, J. Masci, E. Rodolà, and M. M. Bronstein · 2016
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Anisotropic diffusion descriptors
D. Boscaini, J. Masci, E. Rodolà, M. M. Bronstein, and D. Cremers · 2016
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Geometric deep learning: going beyond Euclidean data
M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst · 2016
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SHREC’16: Partial matching of deformable shapes
L. Cosmo, , et al · 2016
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Matching deformable objects in clutter
L. Cosmo, E. Rodolà, J. Masci, A. Torsello, and M. Bronstein · 2016
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Coupled functional maps
D. Eynard, E. Rodola, K. Glashoff, and M. M. Bronstein · 2016
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Y. Aflalo, H. Brezis, and R. Kimmel · 2015
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Recent trends, applications, and perspectives in 3d shape similarity assessment
S. Biasotti, A. Cerri, A. Bronstein, and M. Bronstein · 2015
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Learning class-specific descriptors for deformable shapes using localized spectral convolutional networks
D. Boscaini, J. Masci, S. Melzi, M. M. Bronstein, U. Castellani, and P. Vandergheynst · 2015
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Robust nonrigid registration by convex optimization
Q. Chen and V. Koltun · 2015
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Fast and accurate deep network learning by exponential linear units (elus)
D.-A. Clevert, T. Unterthiner, and S. Hochreiter · 2015
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ADAM: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Functional correspondence by matrix completion
A. Kovnatsky, M. M. Bronstein, X. Bresson, and P. Vandergheynst · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Non-rigid puzzles
O. Litany, E. Rodolà, A. M. Bronstein, M. M. Bronstein, and D. Cremers · 2016
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Dense human body correspondences using convolutional networks
L. Wei, Q. Huang, D. Ceylan, E. Vouga, and H. Li · 2016
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Fully spectral partial shape matching
O. Litany, E. Rodolà, A. M. Bronstein, and M. M. Bronstein · 2017
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Geometric deep learning on graphs and manifolds using mixture model CNNs
F. Monti, D. Boscaini, J. Masci, E. Rodolà, J. Svoboda, and M. M. Bronstein · 2017
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Informative descriptor preservation via commutativity for shape matching,
D. Nogneng and M. Ovsjanikov · 2017
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Partial functional correspondence
E. Rodolà, L. Cosmo, M. M. Bronstein, A. Torsello, and D. Cremers · 2017
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Product manifold filter: Non-rigid shape correspondence via kernel density estimation in the product space
M. Vestner, R. Litman, E. Rodolà, A. Bronstein, and D. Cremers · 2017
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