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We present a novel method for computing correspondences across 3D shapes using unsupervised learning.
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A Concise and Provably Informative Multi-Scale Signature Based on Heat Diffusion
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A survey on shape correspondence
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Functional Maps: A Flexible Representation of Maps Between Shapes
Maks Ovsjanikov, Mirela Ben-Chen, Justin Solomon, Adrian Butscher, and Leonidas Guibas · 2012
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Artiom Kovnatsky, Michael M Bronstein, Alexander M Bronstein, Klaus Glashoff, and Ron Kimmel · 2013
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Map-based exploration of intrinsic shape differences and variability
Raif Rustamov, Maks Ovsjanikov, Omri Azencot, Mirela Ben-Chen, Frederic Chazal, and Leonid Guibas · 2013
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Registration of 3d point clouds and meshes: a survey from rigid to nonrigid
Gary KL Tam, Zhi-Quan Cheng, Yu-Kun Lai, Frank C Langbein, Yonghuai Liu, David Marshall, Ralph R Martin, Xian-Fang Sun, and Paul L Rosin · 2013
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Federica Bogo, Javier Romero, Matthew Loper, and Michael J. Black · 2014
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Supervised descriptor learning for non-rigid shape matching
Etienne Corman, Maks Ovsjanikov, and Antonin Chambolle · 2014
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Functional map networks for analyzing and exploring large shape collections
Qixing Huang, Fan Wang, and Leonidas Guibas · 2014
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Learning spectral descriptors for deformable shape correspondence
Roee Litman and Alexander M Bronstein · 2014
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Dense non-rigid shape correspondence using random forests
Emanuele Rodolà, Samuel Rota Bulo, Thomas Windheuser, Matthias Vestner, and Daniel Cremers · 2014
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Low-resolution remeshing using the localized restricted voronoi diagram
Dong-Ming Yan, Guanbo Bao, Xiaopeng Zhang, and Peter Wonka · 2014
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Martín Abadi, Ashish Agarwal, and Paul Barham et al · 2015
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Learning class-specific descriptors for deformable shapes using localized spectral convolutional networks
Davide Boscaini, Jonathan Masci, Simone Melzi, Michael M Bronstein, Umberto Castellani, and Pierre Vandergheynst · 2015
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Adam: A method for stochastic optimization
J. Ba D.P. Kingma · 2015
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Deblurring and denoising of maps between shapes
Danielle Ezuz and Mirela Ben-Chen · 2017
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Adjoint map representation for shape analysis and matching
Ruqi Huang and Maks Ovsjanikov · 2017
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Deep functional maps: Structured prediction for dense shape correspondence
Or Litany, Tal Remez, Emanuele Rodolà, Alexander M. Bronstein, and Michael M. Bronstein · 2017
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Fully spectral partial shape matching
Or Litany, Emanuele Rodolà, Alex M Bronstein, and Michael M Bronstein · 2017
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Geometric deep learning on graphs and manifolds using mixture model cnns
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Jan Svoboda, and Michael M. Bronstein · 2017
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Informative descriptor preservation via commutativity for shape matching
Dorian Nogneng and Maks Ovsjanikov · 2017
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Functional correspondence by matrix completion
Artiom Kovnatsky, Michael M Bronstein, Xavier Bresson, and Pierre Vandergheynst · 2015
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Geodesic convolutional neural networks on riemannian manifolds
Jonathan Masci, Davide Boscaini, Michael Bronstein, and Pierre Vandergheynst · 2015
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Point-wise map recovery and refinement from functional correspondence
Emanuele Rodolà, M Moeller, and Daniel Cremers · 2015
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Spectral generalized multi-dimensional scaling
Yonathan Aflalo, Anastasia Dubrovina, and Ron Kimmel · 2016
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Recent trends, applications, and perspectives in 3d shape similarity assessment
Silvia Biasotti, Andrea Cerri, A Bronstein, and M Bronstein · 2016
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Learning shape correspondence with anisotropic convolutional neural networks
Davide Boscaini, Jonathan Masci, Emanuele Rodola, and Michael M. Bronstein · 2016
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Matching deformable objects in clutter
Luca Cosmo, Emanuele Rodola, Jonathan Masci, Andrea Torsello, and Michael M Bronstein · 2016
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Computing and processing correspondences with functional maps
Maks Ovsjanikov, Etienne Corman, Michael Bronstein, Emanuele Rodolà, Mirela Ben-Chen, Leonidas Guibas, Frederic Chazal, and Alex Bronstein · 2017
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Partial functional correspondence
Emanuele Rodolà, Luca Cosmo, Michael M Bronstein, Andrea Torsello, and Daniel Cremers · 2017
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Efficient deformable shape correspondence via kernel matching
Matthias Vestner, Zorah Lähner, Amit Boyarski, Or Litany, Ron Slossberg, Tal Remez, Emanuele Rodola, Alex Bronstein, Michael Bronstein, Ron Kimmel, and Daniel 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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Improved functional mappings via product preservation
Dorian Nogneng, Simone Melzi, Emanuele Rodolà, Umberto Castellani, M Bronstein, and Maks Ovsjanikov · 2018
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Multi-directional geodesic neural networks via equivariant convolution
Adrien Poulenard and Maks Ovsjanikov · 2018
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Topological function optimization for continuous shape matching
Adrien Poulenard, Primoz Skraba, and Maks Ovsjanikov · 2018
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Continuous and orientation-preserving correspondences via functional maps
Jing Ren, Adrien Poulenard, Peter Wonka, and Maks Ovsjanikov · 2018
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Kernel functional maps
Larry Wang, Anne Gehre, Michael M Bronstein, and Justin Solomon · 2018
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Vector field map representation for near conformal surface correspondence
Yuexuan Wang, B Liu, K Zhou, and Yu Tong · 2018
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Unsupervised learning of dense shape correspondence
Oshri Halimi, Or Litany, Emanuele Rodol‘a, Alex Bronstein, and Ron Kimmel · 2019
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