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We consider the problem of computing dense correspondences between non-rigid shapes with potentially significant partiality.
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Efficient variants of the icp algorithm
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The trimmed iterative closest point algorithm
Dmitry Chetverikov, Dmitry Svirko, Dmitry Stepanov, and Pavel Krsek · 2002
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Deformation transfer for triangle meshes
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Robust iterative closest point algorithm for registration of point sets with outliers
Shaoyi Du, Jihua Zhu, Nanning Zheng, Yuehu Liu, and Ce Li · 2011
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Blended intrinsic maps
Vladimir G. Kim, Yaron Lipman, and Thomas 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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A game-theoretic approach to deformable shape matching
Emanuele Rodola, Alex M Bronstein, Andrea Albarelli, Filippo Bergamasco, and Andrea Torsello · 2012
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Sparse iterative closest point
Sofien Bouaziz, Andrea Tagliasacchi, and Mark Pauly · 2013
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Coupled quasi-harmonic bases
Artiom Kovnatsky, Michael M Bronstein, Alexander M Bronstein, Klaus Glashoff, and Ron Kimmel · 2013
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A survey of rigid 3d pointcloud registration algorithms
Ben Bellekens, Vincent Spruyt, Rafael Berkvens, and Maarten Weyn · 2014
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Shot: Unique signatures of histograms for surface and texture description
Samuele Salti, Federico Tombari, and Luigi Di Stefano · 2014
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SMPL: A skinned multi-person linear model
Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J. Black · 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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U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P.Fischer, and T. Brox · 2015
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Empirical evaluation of rectified activations in convolutional network
Bing Xu, Naiyan Wang, Tianqi Chen, and Mu Li · 2015
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Recent trends, applications, and perspectives in 3d shape similarity assessment
Silvia Biasotti, Andrea Cerri, Alex Bronstein, and Michael Bronstein · 2016
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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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Shrec’16: Partial matching of deformable shapes
Luca Cosmo, Emanuele Rodola, Michael M Bronstein, Andrea Torsello, Daniel Cremers, and Y Sahillioglu · 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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Non-rigid puzzles
O. Litany, E. Rodolà, A. M. Bronstein, M. M. Bronstein, and D. Cremers · 2016
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Partial functional correspondence
E. Rodolà, L. Cosmo, M. M. Bronstein, A. Torsello, and D. Cremers · 2016
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Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2016
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Diederik P. Kingma and Jimmy Ba · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Deep functional maps: Structured prediction for dense shape correspondence
Or Litany, Tal Remez, Emanuele Rodola, Alex Bronstein, and Michael Bronstein · 2017
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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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Correspondence-free region localization for partial shape similarity via hamiltonian spectrum alignment
Arianna Rampini, Irene Tallini, Maks Ovsjanikov, Alex M Bronstein, and Emanuele Rodola · 2019
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Structured regularization of functional map computations
Jing Ren, Mikhail Panine, Peter Wonka, and Maks Ovsjanikov · 2019
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Unsupervised deep learning for structured shape matching
Jean-Michel Roufosse, Abhishek Sharma, and Maks Ovsjanikov · 2019
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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
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, and Michael M Bronstein · 2017
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Building statistical shape spaces for 3d human modeling
Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt, and Bernt Schiele · 2017
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Charles R. Qi, Li Yi, Hao Su, and Leonidas J. Guibas · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Kpconv: Flexible and deformable convolution for point clouds
Hugues Thomas, Charles R Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, François Goulette, and Leonidas J Guibas · 2019
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PRnet: Self-supervised learning for partial-to-partial registration
Yue Wang and Justin M Solomon · 2019
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A comprehensive survey on geometric deep learning
Wenming Cao, Zhiyue Yan, Zhiquan He, and Zhihai He · 2020
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3d shape registration
Umberto Castellani and Adrien Bartoli · 2020
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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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Wavelet-based heat kernel derivatives: Towards informative localized shape analysis
Maxime Kirgo, Simone Melzi, Giuseppe Patanè, Emanuele Rodolà, and Maks Ovsjanikov · 2020
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Shape correspondence using anisotropic chebyshev spectral cnns
Qinsong Li, Shengjun Liu, Ling Hu, and Xinru Liu · 2020
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Farm: Functional automatic registration method for 3d human bodies
Riccardo Marin, Simone Melzi, Emanuele Rodola, and Umberto Castellani · 2020
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3dregnet: A deep neural network for 3d point registration
G Dias Pais, Srikumar Ramalingam, Venu Madhav Govindu, Jacinto C Nascimento, Rama Chellappa, and Pedro Miraldo · 2020
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Recent advances in shape correspondence
Yusuf Sahillioğlu · 2020
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Weakly supervised deep functional maps for shape matching
Abhishek Sharma and Maks Ovsjanikov · 2020
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Cnns on surfaces using rotation-equivariant features
Ruben Wiersma, Elmar Eisemann, and Klaus Hildebrandt · 2020
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Partial 3d shape functional correspondence via fully spectral eigenvalue alignment and upsampling refinement
Yan Wu, Jun Yang, and Jinlong Zhao · 2020
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Pointcontrast: Unsupervised pre-training for 3d point cloud understanding
Saining Xie, Jiatao Gu, Demi Guo, Charles R. Qi, Leonidas Guibas, and Or Litany · 2020
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Predator: Registration of 3d point clouds with low overlap
Shengyu Huang, Zan Gojcic, Mikhail Usvyatsov, Andreas Wieser, and Konrad Schindler · 2021
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Diffusionnet: Discretization agnostic learning on surfaces
Nicholas Sharp, Souhaib Attaiki, Keenan Crane, and Maks Ovsjanikov · 2021
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A dual iterative refinement method for non-rigid shape matching
Rui Xiang, Rongjie Lai, and Hongkai Zhao · 2021
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