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Shape matching has been a long-studied problem for the computer graphics and vision community.
An iterative image registration technique with an application to stereo vision
Bruce D Lucas and Takeo Kanade · 1981
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Least-squares fitting of two 3-d point sets
K Somani Arun, Thomas S Huang, and Steven D Blostein · 1987
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Conditional random fields: probabilistic models for segmenting and labeling sequence data
JD LAFFERTY · 2001
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Numerical geometry of non-rigid shapes
Alexander M Bronstein, Michael M Bronstein, and Ron Kimmel · 2008
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Aligning point cloud views using persistent feature histograms
Radu Bogdan Rusu, Nico Blodow, Zoltan Csaba Marton, and Michael Beetz · 2008
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Fast point feature histograms (fpfh) for 3d registration
Radu Bogdan Rusu, Nico Blodow, and Michael Beetz · 2009
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Scale-invariant heat kernel signatures for non-rigid shape recognition
Michael M Bronstein and Iasonas Kokkinos · 2010
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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
Mathieu Aubry, Ulrich Schlickewei, and Daniel Cremers · 2011
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Blended intrinsic maps
Vladimir G Kim, Yaron Lipman, and Thomas Funkhouser · 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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Rotational projection statistics for 3d local surface description and object recognition
Yulan Guo, Ferdous Sohel, Mohammed Bennamoun, Min Lu, and Jianwei Wan · 2013
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Trisi: A distinctive local surface descriptor for 3d modeling and object recognition
Yulan Guo, Ferdous Ahmed Sohel, Mohammed Bennamoun, Min Lu, and Jianwei Wan · 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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On the properties of neural machine translation: Encoder–decoder approaches
Kyunghyun Cho, Bart van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio · 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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Dyna: A model of dynamic human shape in motion
Gerard Pons-Moll, Javier Romero, Naureen Mahmood, and Michael J Black · 2015
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Conditional random fields as recurrent neural networks
Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du, Chang Huang, and Philip HS Torr · 2015
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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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A fast and robust local descriptor for 3d point cloud registration
Jiaqi Yang, Zhiguo Cao, and Qian Zhang · 2016
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Dynamic faust: Registering human bodies in motion
Federica Bogo, Javier Romero, Gerard Pons-Moll, and Michael J Black · 2017
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Topology adaptive graph convolutional networks
Jian Du, Shanghang Zhang, Guanhang Wu, José MF Moura, and Soummya Kar · 2017
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Spatial maps: From low rank spectral to sparse spatial functional representations
Andrea Gasparetto, Luca Cosmo, Emanuele Rodola, Michael Bronstein, and Andrea Torsello · 2017
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Learning compact geometric features
Marc Khoury, Qian-Yi Zhou, and Vladlen Koltun · 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
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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
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.
Pointnet++ deep hierarchical feature learning on point sets in a metric space
Charles R Qi, Li Yi, Hao Su, and Leonidas J Guibas · 2017
Cited alongside, same era.
Partial functional correspondence
Emanuele Rodolà, Luca Cosmo, Michael M Bronstein, Andrea Torsello, and Daniel Cremers · 2017
Cited alongside, same era.
Learning from synthetic humans
Gul Varol, Javier Romero, Xavier Martin, Naureen Mahmood, Michael J Black, Ivan Laptev, and Cordelia Schmid · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Unsupervised deep learning for structured shape matching
Jean-Michel Roufosse, Abhishek Sharma, and Maks Ovsjanikov · 2019
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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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Deep closest point: Learning representations for point cloud registration
Yue Wang and Justin M Solomon · 2019
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Dynamic graph cnn for learning on point clouds
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon · 2019
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Three-d safari: Learning to estimate zebra pose, shape, and texture from images” in the wild”
Silvia Zuffi, Angjoo Kanazawa, Tanya Berger-Wolf, and Michael J Black · 2019
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Faster wasserstein distance estimation with the sinkhorn divergence
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Cited alongside, same era.
Product manifold filter: Non-rigid shape correspondence via kernel density estimation in the product space
Matthias Vestner, Roee Litman, Emanuele Rodola, Alex Bronstein, and Daniel Cremers · 2017
Cited alongside, same era.
Scene graph generation by iterative message passing
Danfei Xu, Yuke Zhu, Christopher B Choy, and Li Fei-Fei · 2017
Cited alongside, same era.
3dmatch: Learning local geometric descriptors from rgb-d reconstructions
Andy Zeng, Shuran Song, Matthias Nießner, Matthew Fisher, Jianxiong Xiao, and Thomas Funkhouser · 2017
Cited alongside, same era.
3d menagerie: Modeling the 3d shape and pose of animals
Silvia Zuffi, Angjoo Kanazawa, David W Jacobs, and Michael J Black · 2017
Cited alongside, same era.
Towards sparse hierarchical graph classifiers
Cătălina Cangea, Petar Veličković, Nikola Jovanović, Thomas Kipf, and Pietro Liò · 2018
Cited alongside, same era.
Ppfnet: Global context aware local features for robust 3d point matching
Haowen Deng, Tolga Birdal, and Slobodan Ilic · 2018
Cited alongside, same era.
Lenaic Chizat, Pierre Roussillon, Flavien Léger, François-Xavier Vialard, and Gabriel Peyré · 2020
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The average mixing kernel signature
Luca Cosmo, Giorgia Minello, Michael Bronstein, Luca Rossi, and Andrea Torsello · 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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Correspondence learning via linearly-invariant embedding
Riccardo Marin, Marie-Julie Rakotosaona, Simone Melzi, and Maks Ovsjanikov · 2020
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Nerf: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng · 2020
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Gcnnmatch: Graph convolutional neural networks for multi-object tracking via sinkhorn normalization
Ioannis Papakis, Abhijit Sarkar, and Anuj Karpatne · 2020
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Flot: Scene flow on point clouds guided by optimal transport
Gilles Puy, Alexandre Boulch, and Renaud Marlet · 2020
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Graphite: Graph-induced feature extraction for point cloud registration
Mahdi Saleh, Shervin Dehghani, Benjamin Busam, Nassir Navab, and Federico Tombari · 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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Diffusion is all you need for learning on surfaces
Nicholas Sharp, Souhaib Attaiki, Keenan Crane, and Maks Ovsjanikov · 2020
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Fourier features let networks learn high frequency functions in low dimensional domains
Matthew Tancik, Pratul P Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T Barron, and Ren Ng · 2020
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Learning 3d semantic scene graphs from 3d indoor reconstructions
Johanna Wald, Helisa Dhamo, Nassir Navab, and Federico Tombari · 2020
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Geometric deep learning: Grids, groups, graphs, geodesics, and gauges
Michael M Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković · 2021
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Migs: Meta image generation from scene graphs
Azade Farshad, Sabrina Musatian, Helisa Dhamo, and Nassir Navab · 2021
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Efficient deformable shape correspondence via multiscale spectral manifold wavelets preservation
Ling Hu, Qinsong Li, Shengjun Liu, and Xinru Liu · 2021
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Fast sinkhorn filters: Using matrix scaling for non-rigid shape correspondence with functional maps
Gautam Pai, Jing Ren, Simone Melzi, Peter Wonka, and Maks Ovsjanikov · 2021
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Shape registration in the time of transformers
Giovanni Trappolini, Luca Cosmo, Luca Moschella, Riccardo Marin, Simone Melzi, and Emanuele Rodolà · 2021
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Scenegraphfusion: Incremental 3d scene graph prediction from rgb-d sequences
Shun-Cheng Wu, Johanna Wald, Keisuke Tateno, Nassir Navab, and Federico Tombari · 2021
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Cofinet: Reliable coarse-to-fine correspondences for robust point cloud registration
Hao Yu, Fu Li, Mahdi Saleh, Benjamin Busam, and Slobodan Ilic · 2021
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Distilling holistic knowledge with graph neural networks
Sheng Zhou, Yucheng Wang, Defang Chen, Jiawei Chen, Xin Wang, Can Wang, and Jiajun Bu · 2021
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