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Recent deep networks that directly handle points in a point set, e.g., PointNet, have been state-of-the-art for supervised learning tasks on point clouds such as classification and segmentation.
On visual similarity based 3D model retrieval
D.-Y. Chen, X.-P. Tian, Y.-T. Shen, and M. Ouhyoung · 2003
Earlier work this paper cites.
Rotation invariant spherical harmonic representation of 3d shape descriptors
M. Kazhdan, T. Funkhouser, and S. Rusinkiewicz · 2003
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Surflet-pair-relation histograms: a statistical 3D-shape representation for rapid classification
E. Wahl, U. Hillenbrand, and G. Hirzinger · 2003
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Cross-parameterization and compatible remeshing of 3D models
V. Kraevoy and A. Sheffer · 2004
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Visualizing data using t-sne
L. v. d. Maaten and G. Hinton · 2008
Earlier work this paper cites.
Shape-based recognition of 3d point clouds in urban environments
A. Golovinskiy, V. G. Kim, and T. Funkhouser · 2009
Earlier work this paper cites.
Graph-based segmentation for colored 3d laser point clouds
J. Strom, A. Richardson, and E. Olson · 2010
Earlier work this paper cites.
Convolutional-recursive deep learning for 3D object classification
R. Socher, B. Huval, B. Bath, C. D. Manning, and A. Y. Ng · 2012
Earlier work this paper cites.
3D scene understanding by voxel-CRF
B.-S. Kim, P. Kohli, and S. Savarese · 2013
Earlier work this paper cites.
Label propagation from ImageNet to 3D point clouds
Y. Wang, R. Ji, and S.-F. Chang · 2013
Earlier work this paper cites.
Unsupervised 3D category discovery and point labeling from a large urban environment
Q. Zhang, X. Song, X. Shao, H. Zhao, and R. Shibasaki · 2013
Earlier work this paper cites.
Spectral networks and locally connected networks on graphs
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun · 2014
Earlier work this paper cites.
Object partitioning using local convexity
S. Christoph Stein, M. Schoeler, J. Papon, and F. Worgotter · 2014
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Unsupervised feature learning for 3D scene labeling
K. Lai, L. Bo, and D. Fox · 2014
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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, et al · 2015
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3D object proposals for accurate object class detection
X. Chen, K. Kundu, Y. Zhu, A. G. Berneshawi, H. Ma, S. Fidler, and R. Urtasun · 2015
Earlier work this paper cites.
Learning hierarchical semantic segmentations of LIDAR data
D. Dohan, B. Matejek, and T. Funkhouser · 2015
Earlier work this paper cites.
Convolutional networks on graphs for learning molecular fingerprints
D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams · 2015
Earlier work this paper cites.
Joint 3D object and layout inference from a single RGB-D image
A. Geiger and C. Wang · 2015
Earlier work this paper cites.
Deep convolutional networks on graph-structured data
M. Henaff, J. Bruna, and Y. LeCun · 2015
Earlier work this paper cites.
Geodesic convolutional neural networks on riemannian manifolds
J. Masci, D. Boscaini, M. Bronstein, and P. Vandergheynst · 2015
Cited alongside, same era.
3D convolutional neural networks for landing zone detection from LIDAR
D. Maturana and S. Scherer · 2015
Cited alongside, same era.
Voxnet: A 3D convolutional neural network for real-time object recognition
D. Maturana and S. Scherer · 2015
Cited alongside, same era.
Fast and robust multi-view 3D object recognition in point clouds
G. Pang and U. Neumann · 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.
Diffusion-convolutional neural networks
J. Atwood and D. Towsley · 2016
Cited alongside, same era.
Representation learning and adversarial generation of 3d point clouds
P. Achlioptas, O. Diamanti, I. Mitliagkas, and L. Guibas · 2017
Closest in time.
Unstructured point cloud semantic labeling using deep segmentation networks
A. Boulch, B. L. Saux, and N. Audebert · 2017
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Generative and discriminative voxel modeling with convolutional neural networks
A. Brock, T. Lim, J. M. Ritchie, and N. Weston · 2017
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Geometric deep learning: going beyond euclidean data
M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst · 2017
Closest in time.
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.
Gwcnn: A metric alignment layer for deep shape analysis
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Convolutional neural networks on graphs with fast localized spectral filtering
M. Defferrard, X. Bresson, and P. Vandergheynst · 2016
Cited alongside, same era.
Graph based convolutional neural network
M. Edwards and X. Xie · 2016
Cited alongside, same era.
Learning a predictable and generative vector representation for objects
R. Girdhar, D. F. Fouhey, M. Rodriguez, and A. Gupta · 2016
Cited alongside, same era.
Contour detection in unstructured 3d point clouds
T. Hackel, J. D. Wegner, and K. Schindler · 2016
Cited alongside, same era.
Fast semantic segmentation of 3D point clouds with strongly varying density
T. Hackel, J. D. Wegner, and K. Schindler · 2016
Cited alongside, same era.
Point cloud labeling using 3D convolutional neural network
J. Huang and S. You · 2016
Cited alongside, same era.
D. Ezuz, J. Solomon, V. G. Kim, and M. Ben-Chen · 2017
Closest in time.
A point set generation network for 3D object reconstruction from a single image
H. Fan, H. Su, and L. Guibas · 2017
Closest in time.
A generalization of convolutional neural networks to graph-structured data
Y. Hechtlinger, P. Chakravarti, and J. Qin · 2017
Closest in time.
Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
Closest in time.
Escape from cells: Deep Kd-networks for the recognition of 3D point cloud models
R. Klokov and V. Lempitsky · 2017
Closest in time.
Cayleynets: Graph convolutional neural networks with complex rational spectral filters
R. Levie, F. Monti, X. Bresson, and M. M. Bronstein · 2017
Closest in time.
Geometric deep learning on graphs and manifolds using mixture model CNNs
F. Monti, D. Boscaini, J. Masci, E. Rodolà, J. Svoboda, and M. M. Bronstein · 2017
Closest in time.
Pointnet: Deep learning on point sets for 3D classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
Closest in time.
Pointnet++: Deep hierarchical feature learning on point sets in a metric space
C. R. Qi, L. Yi, H. Su, and L. J. Guibas · 2017
Closest in time.
Deep learning with sets and point clouds
S. Ravanbakhsh, J. Schneider, and B. Poczos · 2017
Closest in time.
Octnet: Learning deep 3D representations at high resolutions
G. Riegler, A. O. Ulusoys, and A. Geiger · 2017
Closest in time.
Dynamic edge-conditioned filters in convolutional neural networks on graphs
M. Simonovsky and N. Komodakis · 2017
Closest in time.
3d shape segmentation via shape fully convolutional networks
P. Wang, Y. Gan, P. Shui, F. Yu, Y. Zhang, S. Chen, and Z. Sun · 2017
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Deep sets
M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R. Salakhutdinov, and A. Smola · 2017
Closest in time.
Mining point cloud local structures by kernel correlation and graph pooling
Y. Shen, C. Feng, Y. Yang, and D. Tian · 2018
Closest in time.