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Few prior works study deep learning on point sets.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Discrete differential-geometry operators for triangulated 2-manifolds
M. Meyer, M. Desbrun, P. Schröder, A. H. Barr, et al · 2002
Earlier work this paper cites.
Best practices for convolutional neural networks applied to visual document analysis
P. Y. Simard, D. Steinkraus, and J. C. Platt · 2003
Earlier work this paper cites.
Estimating surface normals in noisy point cloud data
N. J. MITRA, A. NGUYEN, and L. GUIBAS · 2004
Earlier work this paper cites.
Classification and segmentation of terrestrial laser scanner point clouds using local variance information
D. Belton and D. D. Lichti · 2006
Earlier work this paper cites.
Point-based multiscale surface representation
M. Pauly, L. P. Kobbelt, and M. Gross · 2006
Earlier work this paper cites.
Interior distance using barycentric coordinates
R. M. Rustamov, Y. Lipman, and T. Funkhouser · 2009
Earlier work this paper cites.
A concise and provably informative multi-scale signature based on heat diffusion
J. Sun, M. Ovsjanikov, and L. Guibas · 2009
Earlier work this paper cites.
The wave kernel signature: A quantum mechanical approach to shape analysis
M. Aubry, U. Schlickewei, and D. Cremers · 2011
Earlier work this paper cites.
Dimensionality based scale selection in 3d lidar point clouds
J. Demantké, C. Mallet, N. David, and B. Vallet · 2011
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.
Spectral networks and locally connected networks on graphs
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun · 2013
Cited alongside, same era.
Towards 3d lidar point cloud registration improvement using optimal neighborhood knowledge
A. Gressin, C. Mallet, J. Demantké, and N. David · 2013
Cited alongside, same era.
M. Lin, Q. Chen, and S. Yan · 2013
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
ShapeNet: An Information-Rich 3D Model Repository
A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu · 2015
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Later among the works it cites.
Structure sensor-3d scanning, augmented reality, and more for mobile devices, 2016
I. Occipital · 2016
Later among the works it cites.
Pointnet: Deep learning on point sets for 3d classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2016
Later among the works it cites.
Volumetric and multi-view cnns for object classification on 3d data
C. R. Qi, H. Su, M. Nießner, A. Dai, M. Yan, and L. Guibas · 2016
Later among the works it cites.
Octnet: Learning deep 3d representations at high resolutions
G. Riegler, A. O. Ulusoys, and A. Geiger · 2016
Later among the works it cites.
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Cited alongside, same era.
Non-rigid 3D Shape Retrieval
Z. Lian, J. Zhang, S. Choi, H. ElNaghy, J. El-Sana, T. Furuya, A. Giachetti, R. A. Guler, L. Lai, C. Li, H. Li, F. A. Limberger, R. Martin, R. U. Nakanishi, A. P. Neto, L. G. Nonato, R. Ohbuchi, K. Pevzner, D. Pickup, P. Rosin, A. Sharf, L. Sun, X. Sun, S. Tari, G. Unal, and R. C. Wilson · 2015
Cited alongside, same era.
Geodesic convolutional neural networks on riemannian manifolds
J. Masci, D. Boscaini, M. Bronstein, and P. Vandergheynst · 2015
Cited alongside, same era.
Multi-view convolutional neural networks for 3d shape recognition
H. Su, S. Maji, E. Kalogerakis, and E. G. Learned-Miller · 2015
Cited alongside, same era.
Order matters: Sequence to sequence for sets
O. Vinyals, S. Bengio, and M. Kudlur · 2015
Cited alongside, same era.
Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers
M. Weinmann, B. Jutzi, S. Hinz, and C. Mallet · 2015
Cited alongside, same era.
3d shapenets: A deep representation for volumetric shapes
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
D. Kingma and J. Ba
Cited in the paper.
A scalable active framework for region annotation in 3d shape collections
L. Yi, V. G. Kim, D. Ceylan, I.-C. Shen, M. Yan, H. Su, C. Lu, Q. Huang, A. Sheffer, and L. Guibas · 2016
Later among the works it cites.
Syncspeccnn: Synchronized spectral cnn for 3d shape segmentation
L. Yi, H. Su, X. Guo, and L. Guibas · 2016
Later among the works it cites.
Scannet: Richly-annotated 3d reconstructions of indoor scenes
A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner · 2017
Closest in time.
Deep learning with geodesic moments for 3d shape classification
L. Luciano and A. B. Hamza · 2017
Closest in time.
O-cnn: Octree-based convolutional neural networks for 3d shape analysis
P.-S. WANG, Y. LIU, Y.-X. GUO, C.-Y. SUN, and X. TONG · 2017
Closest in time.