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We present a detail-driven deep neural network for point set upsampling.
Smooth subdivision surfaces based on triangles
C. Loop · 1987
Earlier work this paper cites.
Surface reconstruction from unorganized points
H. Hoppe, T. DeRose, T. Duchamp, J. McDonald, and W. Stuetzle · 1992
Earlier work this paper cites.
Triangulations in CGAL
J.-D. Boissonnat, O. Devillers, S. Pion, M. Teillaud, and M. Yvinec · 2002
Earlier work this paper cites.
Computing and rendering point set surfaces
M. Alexa, J. Behr, D. Cohen-Or, S. Fleishman, D. Levin, and C. T. Silva · 2003
Earlier work this paper cites.
Parameterization-free projection for geometry reconstruction
Y. Lipman, D. Cohen-Or, D. Levin, and H. Tal-Ezer · 2007
Earlier work this paper cites.
Meshlab: an open-source mesh processing tool
P. Cignoni, M. Callieri, M. Corsini, M. Dellepiane, F. Ganovelli, and G. Ranzuglia · 2008
Earlier work this paper cites.
Consolidation of unorganized point clouds for surface reconstruction
H. Huang, D. Li, H. Zhang, U. Ascher, and D. Cohen-Or · 2009
Earlier work this paper cites.
MNIST handwritten digit database
Y. LeCun and C. Cortes · 2010
Earlier work this paper cites.
Efficient and flexible sampling with blue noise properties of triangular meshes
M. Corsini, P. Cignoni, and R. Scopigno · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
A benchmark for surface reconstruction
M. Berger, J. A. Levine, L. G. Nonato, G. Taubin, and C. T. Silva · 2013
Earlier work this paper cites.
Edge-aware point set resampling
H. Huang, S. Wu, M. Gong, D. Cohen-Or, U. Ascher, and H. Zhang · 2013
Earlier work this paper cites.
Screened poisson surface reconstruction
M. Kazhdan and H. Hoppe · 2013
Earlier work this paper cites.
Conditional generative adversarial nets
M. Mirza and S. Osindero · 2014
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
Earlier work this paper cites.
Deep points consolidation
S. Wu, H. Huang, M. Gong, M. Zwicker, and D. Cohen-Or · 2015
Earlier work this paper cites.
3d shapenets: A deep representation for volumetric shapes
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao · 2015
Earlier work this paper cites.
Image super-resolution using deep convolutional networks
C. Dong, C. C. Loy, K. He, and X. Tang · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Deep networks with stochastic depth
G. Huang, Y. Sun, Z. Liu, D. Sedra, and K. Q. Weinberger · 2016
Earlier work this paper cites.
Accurate image super-resolution using very deep convolutional networks
J. Kim, J. Kwon Lee, and K. Mu Lee · 2016
Earlier work this paper cites.
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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Balanced two-stage residual networks for image super-resolution
Y. Fan, H. Shi, J. Yu, D. Liu, W. Han, H. Yu, Z. Wang, X. Wang, and T. S. Huang · 2017
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Densely connected convolutional networks
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger · 2017
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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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Deep laplacian pyramid networks for fast and accurate superresolution
W.-S. Lai, J.-B. Huang, N. Ahuja, and M.-H. Yang · 2017
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Photo-realistic single image super-resolution using a generative adversarial network
Progressive growing of gans for improved quality, stability, and variation
T. Karras, T. Aila, S. Laine, and J. Lehtinen · 2018
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So-net: Self-organizing network for point cloud analysis
J. Li, B. M. Chen, and G. H. Lee · 2018
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Y. Li, R. Bu, M. Sun, and B. Chen · 2018
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Multi-scale context intertwining for semantic segmentation
D. Lin, Y. Ji, D. Lischinski, D. Cohen-Or, and H. Huang · 2018
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X. Liu, Z. Han, Y.-S. Liu, and M. Zwicker · 2018
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C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. P. Aitken, A. Tejani, J. Totz, Z. Wang, et al · 2017
Cited alongside, same era.
Frustum pointnets for 3D object detection from rgb-d data
C. R. Qi, W. Liu, C. Wu, H. Su, and L. J. Guibas · 2017
Cited alongside, same era.
PointNet: Deep learning on point sets for 3D classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
Cited alongside, same era.
PointNet++: Deep hierarchical feature learning on point sets in a metric space
C. R. Qi, L. Yi, H. Su, and L. J. Guibas · 2017
Cited alongside, same era.
Learning representations and generative models for 3D point clouds
P. Achlioptas, O. Diamanti, I. Mitliagkas, and L. Guibas · 2018
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Point convolutional neural networks by extension operators
M. Atzmon, H. Maron, and Y. Lipman · 2018
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PPF-FoldNet: Unsupervised learning of rotation invariant 3D local descriptors
H. Deng, T. Birdal, and S. Ilic · 2018
Cited alongside, same era.
D. Rethage, J. Wald, J. Sturm, N. Navab, and F. Tombari · 2018
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PointProNets: Consolidation of point clouds with convolutional neural networks
R. Roveri, A. C. Öztireli, I. Pandele, and M. Gross · 2018
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Mining point cloud local structures by kernel correlation and graph pooling
Y. Shen, C. Feng, Y. Yang, and D. Tian · 2018
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Adaptive O-CNN: A patch-based deep representation of 3D shapes
P.-S. Wang, C.-Y. Sun, Y. Liu, and X. Tong · 2018
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High-resolution image synthesis and semantic manipulation with conditional GANs
T.-C. Wang, M.-Y. Liu, J.-Y. Zhu, A. Tao, J. Kautz, and B. Catanzaro · 2018
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A fully progressive approach to single-image super-resolution
Y. Wang, F. Perazzi, B. McWilliams, A. Sorkine-Hornung, O. Sorkine-Hornung, and C. Schroers · 2018
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Dynamic graph cnn for learning on point clouds
Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon · 2018
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Spidercnn: Deep learning on point sets with parameterized convolutional filters
Y. Xu, T. Fan, M. Xu, L. Zeng, and Y. Qiao · 2018
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Foldingnet: Point cloud auto-encoder via deep grid deformation
Y. Yang, C. Feng, Y. Shen, and D. Tian · 2018
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P2p-net: bidirectional point displacement net for shape transform
K. Yin, H. Huang, D. Cohen-Or, and H. Zhang · 2018
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Ec-net: an edge-aware point set consolidation network
L. Yu, X. Li, C.-W. Fu, D. Cohen-Or, and P.-A. Heng · 2018
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Pu-net: Point cloud upsampling network
L. Yu, X. Li, C.-W. Fu, D. Cohen-Or, and P.-A. Heng · 2018
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Pcn: Point completion network
W. Yuan, T. Khot, D. Held, C. Mertz, and M. Hebert · 2018
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Data-driven upsampling of point clouds
W. Zhang, H. Jiang, Z. Yang, S. Yamakawa, K. Shimada, and L. B. Kara · 2018
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Gun: Gradual upsampling network for single image super-resolution
Y. Zhao, G. Li, W. Xie, W. Jia, H. Min, and X. Liu · 2018
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