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We address the problem of 3D rotation equivariance in convolutional neural networks.
3d shapenets: A deep representation for volumetric shapes
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Mathematical Methods for Physicists
Arfken, G.: · 1966
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The canonical coordinates method for pattern deformation: Theoretical and computational considerations
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Computing fourier transforms and convolutions on the 2-sphere
Driscoll, J.R., Healy, D.M.: · 1994
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Canonical decomposition of steerable functions
Hel-Or, Y., Teo, P.C.: · 1996
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Three-Dimensional Geometry and Topology, Volume 1: Volume 1
Thurston, W.P.: · 1997
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Harmonic 3d shape matching
Kazhdan, M., Funkhouser, T.: · 2002
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Ffts for the 2-sphere-improvements and variations
Healy, D.M., Rockmore, D.N., Kostelec, P.J., Moore, S.: · 2003
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Recognizing objects in range data using regional point descriptors
Frome, A., Huber, D., Kolluri, R., Bülow, T., Malik, J.: · 2004
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Multi-fourier spectra descriptor and augmentation with spectral clustering for 3d shape retrieval
Tatsuma, A., Aono, M.: · 2009
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Spherical correlation of visual representations for 3d model retrieval
Makadia, A., Daniilidis, K.: · 2010
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Learning stable group invariant representations with convolutional networks
Bruna, J., Szlam, A., LeCun, Y.: · 2013
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Spectral networks and locally connected networks on graphs
Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: · 2013
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Spectral networks and locally connected networks on graphs
Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: · 2013
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Deep symmetry networks
Gens, R., Domingos, P.M.: · 2014
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Multi-view convolutional neural networks for 3d shape recognition
Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.: · 2015
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Rotation-invariant convolutional neural networks for galaxy morphology prediction
Dieleman, S., Willett, K.W., Dambre, J.: · 2015
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Understanding image representations by measuring their equivariance and equivalence
Lenc, K., Vedaldi, A.: · 2015
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Geodesic convolutional neural networks on riemannian manifolds
Masci, J., Boscaini, D., Bronstein, M., Vandergheynst, P.: · 2015
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Voxnet: A 3d convolutional neural network for real-time object recognition
Maturana, D., Scherer, S.: · 2015
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Semi-supervised classification with graph convolutional networks
Kipf, T.N., Welling, M.: · 2016
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Learning shape correspondence with anisotropic convolutional neural networks
Boscaini, D., Masci, J., Rodolà, E., Bronstein, M.: · 2016
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Geometric deep learning on graphs and manifolds using mixture model cnns
Monti, F., Boscaini, D., Masci, J., Rodolà, E., Svoboda, J., Bronstein, M.M.: · 2016
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Gift: A real-time and scalable 3d shape search engine
Bai, S., Bai, X., Zhou, Z., Zhang, Z., Jan Latecki, L.: · 2016
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Deep aggregation of local 3d geometric features for 3d model retrieval
Furuya, T., Ohbuchi, R.: · 2016
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Spectral representations for convolutional neural networks
Rippel, O., Snoek, J., Adams, R.P.: · 2015
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Shapenet: An information-rich 3d model repository
Chang, A.X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., Xiao, J., Yi, L., Yu, F.: · 2015
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Facenet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., Philbin, J.: · 2015
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Group equivariant convolutional networks
Cohen, T.S., Welling, M.: · 2016
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Volumetric and multi-view cnns for object classification on 3d data
Qi, C.R., Su, H., Nießner, M., Dai, A., Yan, M., Guibas, L.J.: · 2016
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Harmonic networks: Deep translation and rotation equivariance
Worrall, D.E., Garbin, S.J., Turmukhambetov, D., Brostow, G.J.: · 2016
Cited alongside, same era.
Harmonic networks: Deep translation and rotation equivariance
Worrall, D.E., Garbin, S.J., Turmukhambetov, D., Brostow, G.J.: · 2017
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Geometric deep learning: going beyond euclidean data
Bronstein, M.M., Bruna, J., LeCun, Y., Szlam, A., Vandergheynst, P.: · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: · 2017
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Shrec’17 track: Large-scale 3d shape retrieval from shapenet core55
Savva, M., Yu, F., Su, H., Kanezaki, A., Furuya, T., Ohbuchi, R., Zhou, Z., Yu, R., Bai, S., Bai, X., Aono, M., Tatsuma, A., Thermos, S., Axenopoulos, A., Papadopoulos, G.T., Daras, P., Deng, X., Lian, Z., Li, B., Johan, H., Lu, Y., Mk, S.: · 2017
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Oriented response networks
Zhou, Y., Ye, Q., Qiu, Q., Jiao, J.: · 2017
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: · 2017
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Spherical CNNs
Cohen, T.S., Geiger, M., Köhler, J., Welling, M.: · 2018
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Rotationnet: Joint object categorization and pose estimation using multiviews from unsupervised viewpoints
Kanezaki, A., Matsushita, Y., Nishida, Y.: · 2018
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Spatial transformer networks
Jaderberg, M., Simonyan, K., Zisserman, A., et al.: · 2025
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