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Learning and analyzing 3D point clouds with deep networks is challenging due to the sparseness and irregularity of the data.
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ShapeNet: An information-rich 3D model repository
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Geodesic convolutional neural networks on Riemannian manifolds
J. Masci, D. Boscaini, M. Bronstein, and P. Vandergheynst · 2015
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Voxnet: A 3D convolutional neural network for real-time object recognition
D. Maturana and S. Scherer · 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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Deep points consolidation
S. Wu, H. Huang, M. Gong, M. Zwicker, and D. Cohen-Or · 2015
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3D ShapeNets: A deep representation for volumetric shapes
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao · 2015
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Holistically-nested edge detection
S. Xie and Z. Tu · 2015
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Escape from cells: deep Kd-Networks for the recognition of 3D point cloud models
R. Klokov and V. Lempitsky · 2017
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Photo-realistic single image super-resolution using a generative adversarial network
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Learning efficient point cloud generation for dense 3D object reconstruction
C.-H. Lin, C. Kong, and S. Lucey · 2017
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PointNet: Deep learning on point sets for 3D classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
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PointNet++: Deep hierarchical feature learning on point sets in a metric space
C. R. Qi, L. Yi, H. Su, and L. J. Guibas · 2017
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OctNet: Learning deep 3D representations at high resolutions
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Image super-resolution using deep convolutional networks
C. Dong, C. C. Loy, K. He, and X. Tang · 2016
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Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network
W. Shi, J. Caballero, F. Huszár, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang · 2016
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Aggregated residual transformations for deep neural networks
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He · 2016
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[Online; accessed on 14-November-2017]
Visionair · 2017
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Shape completion using 3D-Encoder-Predictor CNNs and shape synthesis
A. Dai, C. R. Qi, and M. Niessner · 2017
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A point set generation network for 3D object reconstruction from a single image
H. Fan, H. Su, and L. J. Guibas · 2017
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G. Riegler, A. O. Ulusoys, and A. Geiger · 2017
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ShuffleNet: An extremely efficient convolutional neural network for mobile devices
X. Zhang, X. Zhou, M. Lin, and J. Sun · 2017
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AtlasNet: A papier-mâché approach to learning 3D surface generation
T. Groueix, M. Fisher, V. G. Kim, B. C. Russell, and M. Aubry · 2018
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PCPNet: Learning local shape properties from raw point clouds
P. Guerrero, Y. Kleiman, M. Ovsjanikov, and N. J. Mitra · 2018
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Pointwise convolutional neural networks
B.-S. Hua, M.-K. Tran, and S.-K. Yeung · 2018
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Frustum PointNets for 3D object detection from RGB-D data
C. R. Qi, W. Liu, C. Wu, H. Su, and L. J. Guibas · 2018
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SGPN: Similarity group proposal network for 3D point cloud instance segmentation
W. Wang, R. Yu, Q. Huang, and U. Neumann · 2018
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