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We propose a novel unsupervised learning approach to 3D shape correspondence that builds a multiscale matching pipeline into a deep neural network.
On the scaling of multidimensional matrices
Joel Franklin and Jens Lorenz · 1989
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Computing discrete minimal surfaces and their conjugates
Ulrich Pinkall and Konrad Polthier · 1993
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The symmetric eigenvalue problem
Beresford N Parlett · 1998
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Mips: An efficient global parametrization method
Kai Hormann and Günther Greiner · 2000
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Scape: shape completion and animation of people
Dragomir Anguelov, Praveen Srinivasan, Daphne Koller, Sebastian Thrun, Jim Rodgers, and James Davis · 2005
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A review of recent range image registration methods with accuracy evaluation
Joaquim Salvi, Carles Matabosch, David Fofi, and Josep Forest · 2007
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A concise and provably informative multi-scale signature based on heat diffusion
Jian Sun, Maks Ovsjanikov, and Leonidas Guibas · 2009
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Unique signatures of histograms for local surface description
Federico Tombari, Samuele Salti, and Luigi Di Stefano · 2010
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The wave kernel signature: A quantum mechanical approach to shape analysis
Matthieu Aubry, Ulrich Schlickewei, and Daniel Cremers · 2011
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Blended intrinsic maps
Vladimir G Kim, Yaron Lipman, and Thomas A Funkhouser · 2011
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A survey on shape correspondence
Oliver van Kaick, Hao Zhang, Ghassan Hamarneh, and Daniel Cohen-Or · 2011
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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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Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2013
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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FAUST: Dataset and evaluation for 3D mesh registration
Federica Bogo, Javier Romero, Matthew Loper, and Michael J. Black · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Deep convolutional networks on graph-structured data. arxiv (2015)
Mikael Henaff, Joan Bruna, and Yann LeCun · 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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Learning shape correspondence with anisotropic convolutional neural networks
Davide Boscaini, Jonathan Masci, Emanuele Rodolà, and Michael Bronstein · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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Learning latent permutations with gumbel-sinkhorn networks
Gonzalo Mena, David Belanger, Scott Linderman, and Jasper Snoek · 2018
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Multi-directional geodesic neural networks via equivariant convolution
Adrien Poulenard 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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Unsupervised learning of dense shape correspondence
Oshri Halimi, Or Litany, Emanuele Rodola, Alex M Bronstein, and Ron Kimmel · 2019
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Zoomout: Spectral upsampling for efficient shape correspondence
Simone Melzi, Jing Ren, Emanuele Rodolà, Abhishek Sharma, Peter Wonka, and Maks Ovsjanikov · 2019
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Thomas N Kipf and Max Welling · 2016
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Non-rigid puzzles
Or Litany, Emanuele Rodolà, Alex M Bronstein, Michael M Bronstein, and Daniel Cremers · 2016
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Partial functional correspondence
Emanuele Rodolà, Luca Cosmo, Michael Bronstein, Andrea Torsello, and Daniel Cremers · 2016
Cited alongside, same era.
Geometric deep learning: going beyond euclidean data
Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst · 2017
Cited alongside, same era.
Deep functional maps: Structured prediction for dense shape correspondence
Or Litany, Tal Remez, Emanuele Rodolà, Alex Bronstein, and Michael Bronstein · 2017
Cited alongside, same era.
Geometric deep learning on graphs and manifolds using mixture model cnns
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, and Michael M Bronstein · 2017
Cited alongside, same era.
Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
Cited alongside, same era.
Jean-Michel Roufosse, Abhishek Sharma, and Maks Ovsjanikov · 2019
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Recent advances in shape correspondence
Yusuf Sahillioğlu · 2019
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Deep geometric functional maps: Robust feature learning for shape correspondence
Nicolas Donati, Abhishek Sharma, and Maks Ovsjanikov · 2020
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Smooth shells: Multi-scale shape registration with functional maps
Marvin Eisenberger, Zorah Lahner, and Daniel Cremers · 2020
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Scan of discobolus at the british museum
British Museum London · 2020
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Cnns on surfaces using rotation-equivariant features
Klaus Hildebrandt Ruben Wiersma, Elmar Eisemann · 2020
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Superglue: Learning feature matching with graph neural networks
Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich · 2020
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
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Rpm-net: Robust point matching using learned features
Zi Jian Yew and Gim Hee Lee · 2020
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