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The majority of descriptor-based methods for geometric processing of non-rigid shape rely on hand-crafted descriptors.
Kpconv: Flexible and deformable convolution for point clouds
Thomas, H., Qi, C.R., Deschaud, J.E., Marcotegui, B., Goulette, F., Guibas, L.J., 2019 · 1904
Earlier work this paper cites.
Quaternion equivariant capsule networks for 3d point clouds
Zhao, Y., Birdal, T., Lenssen, J.E., Menegatti, E., Guibas, L., Tombari, F., 2019 · 1912
Earlier work this paper cites.
Using spin images for efficient object recognition in cluttered 3d scenes
Johnson, A.E., Hebert, M., 1999 · 1999
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Fast marching farthest point sampling
Moenning, C., Dodgson, N.A., 2003 · 2003
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Scape: shape completion and animation of people, in: ACM transactions on graphics (TOG), ACM. pp. 408–416
Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., Davis, J., 2005 · 2005
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Shape retrieval contest 2007: Watertight models track
Giorgi, D., Biasotti, S., Paraboschi, L., · 2007
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Laplace-beltrami eigenfunctions for deformation invariant shape representation, in: Proceedings of the fifth Eurographics symposium on Geometry processing, Eurographics Association. pp. 225–233
Rustamov, R.M., 2007 · 2007
Earlier work this paper cites.
Persistent Point Feature Histograms for 3D Point Clouds, in: Proceedings of the 10th International Conference on Intelligent Autonomous Systems (IAS-10), Baden-Baden, Germany
Rusu, R.B., Marton, Z.C., Blodow, N., Beetz, M., 2008 · 2008
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Articulated mesh animation from multi-view silhouettes, in: ACM Transactions on Graphics (TOG), ACM. p. 97
Vlasic, D., Baran, I., Matusik, W., Popović, J., 2008 · 2008
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Fast point feature histograms (fpfh) for 3d registration, in: Robotics and Automation, 2009. ICRA’09. IEEE International Conference on, IEEE. pp. 3212–3217
Rusu, R.B., Blodow, N., Beetz, M., 2009 · 2009
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A concise and provably informative multi-scale signature based on heat diffusion, in: Computer graphics forum, Wiley Online Library. pp. 1383–1392
Sun, J., Ovsjanikov, M., Guibas, L., 2009 · 2009
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Scale-invariant heat kernel signatures for non-rigid shape recognition, in: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE. pp. 1704–1711
Bronstein, M.M., Kokkinos, I., 2010 · 2010
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Learning 3d mesh segmentation and labeling, in: ACM Transactions on Graphics (TOG), ACM. p. 102
Kalogerakis, E., Hertzmann, A., Singh, K., 2010 · 2010
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On the repeatability and quality of keypoints for local feature-based 3d object retrieval from cluttered scenes
Mian, A., Bennamoun, M., Owens, R., 2010 · 2010
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Rectified linear units improve restricted boltzmann machines, in: Proceedings of the 27th international conference on machine learning (ICML-10), pp. 807–814
Nair, V., Hinton, G.E., 2010 · 2010
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The wave kernel signature: A quantum mechanical approach to shape analysis, in: 2011 IEEE international conference on computer vision workshops (ICCV workshops), IEEE. pp. 1626–1633
Aubry, M., Schlickewei, U., Cremers, D., 2011 · 2011
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Shape google: Geometric words and expressions for invariant shape retrieval
Bronstein, A.M., Bronstein, M.M., Guibas, L.J., Ovsjanikov, M., 2011 · 2011
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Blended intrinsic maps, in: ACM Transactions on Graphics (TOG), ACM. p. 79
Kim, V.G., Lipman, Y., Funkhouser, T., 2011 · 2011
Earlier work this paper cites.
On the repeatability of the local reference frame for partial shape matching, in: 2011 International Conference on Computer Vision, IEEE. pp. 2244–2251
Petrelli, A., Di Stefano, L., 2011 · 2011
Cited alongside, same era.
Spectral networks and locally connected networks on graphs
Bruna, J., Zaremba, W., Szlam, A., LeCun, Y., 2013 · 2013
Cited alongside, same era.
Rops: A local feature descriptor for 3d rigid objects based on rotational projection statistics, in: Communications, Signal Processing, and their Applications (ICCSPA), 2013 1st International Conference on, IEEE. pp. 1–6
Guo, Y., Sohel, F.A., Bennamoun, M., Wan, J., Lu, M., 2013 · 2013
Cited alongside, same era.
Learning spectral descriptors for deformable shape correspondence
Litman, R., Bronstein, A.M., 2013 · 2013
Cited alongside, same era.
Faust: Dataset and evaluation for 3d mesh registration, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3794–3801
Learning compact geometric features, in: IEEE International Conference on Computer Vision (ICCV)
Khoury, M., Zhou, Q.Y., Koltun, V., 2017 · 2017
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Convolutional neural networks on surfaces via seamless toric covers
Maron, H., Galun, M., Aigerman, N., Trope, M., dym, N., , Yumer, E., Kim, V., Lipman, Y., 2017 · 2017
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Geometric deep learning on graphs and manifolds using mixture model cnns, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5115–5124
Monti, F., Boscaini, D., Masci, J., Rodola, E., Svoboda, J., Bronstein, M.M., 2017 · 2017
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3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Zeng, A., Song, S., Nießner, M., Fisher, M., Xiao, J., Funkhouser, T., 2017 · 2017
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Bogo, F., Romero, J., Loper, M., Black, M.J., 2014 · 2014
Cited alongside, same era.
Self-similarity for accurate compression of point sampled surfaces, in: Computer Graphics Forum, Wiley Online Library. pp. 155–164
Digne, J., Chaine, R., Valette, S., 2014 · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D.P., Ba, J., 2014 · 2014
Cited alongside, same era.
Dense non-rigid shape correspondence using random forests, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4177–4184
Rodolà, E., Rota Bulo, S., Windheuser, T., Vestner, M., Cremers, D., 2014 · 2014
Cited alongside, same era.
Deep convolutional networks on graph-structured data
Henaff, M., Bruna, J., LeCun, Y., 2015 · 2015
Cited alongside, same era.
Repeatable local coordinate frames for 3d human motion tracking: From rigid to non-rigid, in: 2015 International Conference on 3D Vision, IEEE. pp. 371–379
Huang, C.H., Tombari, F., Navab, N., 2015 · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C., 2015 · 2015
Cited alongside, same era.
Geodesic convolutional neural networks on riemannian manifolds, in: Proceedings of the IEEE international conference on computer vision workshops, pp. 37–45
Masci, J., Boscaini, D., Bronstein, M., Vandergheynst, P., 2015 · 2015
Cited alongside, same era.
Atzmon, M., Maron, H., Lipman, Y., 2018 · 2018
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Self-supervised learning of dense shape correspondence
Halimi, O., Litany, O., Rodol‘a, E., Bronstein, A., Kimmel, R., 2018 · 2018
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Feastnet: Feature-steered graph convolutions for 3d shape analysis, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2598–2606
Verma, N., Boyer, E., Verbeek, J., 2018 · 2018
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Deep parametric continuous convolutional neural networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2589–2597
Wang, S., Suo, S., Ma, W.C., Pokrovsky, A., Urtasun, R., 2018 · 2018
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Fully convolutional geometric features, in: The IEEE International Conference on Computer Vision (ICCV), pp. 8958–8966
Choy, C., Park, J., Koltun, V., 2019 · 2019
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3d local features for direct pairwise registration, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Deng, H., Birdal, T., Ilic, S., 2019 · 2019
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The perfect match: 3d point cloud matching with smoothed densities, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5545–5554
Gojcic, Z., Zhou, C., Wegner, J.D., Wieser, A., 2019 · 2019
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Meshcnn: A network with an edge
Hanocka, R., Hertz, A., Fish, N., Giryes, R., Fleishman, S., Cohen-Or, D., 2019 · 2019
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Texturenet: Consistent local parametrizations for learning from high-resolution signals on meshes, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4440–4449
Huang, J., Zhang, H., Yi, L., Funkhouser, T., Nießner, M., Guibas, L.J., 2019 · 2019
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Abc: A big cad model dataset for geometric deep learning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9601–9611
Koch, S., Matveev, A., Jiang, Z., Williams, F., Artemov, A., Burnaev, E., Alexa, M., Zorin, D., Panozzo, D., 2019 · 2019
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Gframes: Gradient-based local reference frame for 3d shape matching, in: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Melzi, S., Spezialetti, R., Tombari, F., Bronstein, M.M., Stefano, L.D., Rodola, E., 2019 · 2019
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Multi-directional geodesic neural networks via equivariant convolution
Poulenard, A., Ovsjanikov, M., 2019 · 2019
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