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Three dimensional (3D) object recognition is becoming a key desired capability for many computer vision systems such as autonomous vehicles, service robots and surveillance drones to operate more effectively in unstructured environments.
Unsupervised feature learning for classification of outdoor 3d scans
M. De Deuge, A. Quadros, C. Hung, and B. Douillard · 2013
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Sampling-based robot motion planning: A review
M. Elbanhawi and M. Simic · 2014
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Deeply-supervised nets
C.-Y. Lee, S. Xie, P. Gallagher, Z. Zhang, and Z. Tu · 2015
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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
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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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Multi-view convolutional neural networks for 3d shape recognition
H. Su, S. Maji, E. Kalogerakis, and E. Learned-Miller · 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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Generative and discriminative voxel modeling with convolutional neural networks
A. Brock, T. Lim, J. M. Ritchie, and N. Weston · 2016
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Multi-view 3d models from single images with a convolutional network
M. Tatarchenko, A. Dosovitskiy, and T. Brox · 2016
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Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
J. Wu, C. Zhang, T. Xue, B. Freeman, and J. Tenenbaum · 2016
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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
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A triangle histogram for object classification by tactile sensing
M. M. Zhang, M. D. Kennedy, M. A. Hsieh, and K. Daniilidis · 2016
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Efficient online segmentation for sparse 3d laser scans
I. Bogoslavskyi and C. Stachniss · 2017
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Shape completion using 3d-encoder-predictor cnns and shape synthesis
A. Dai, C. Ruizhongtai Qi, and M. Nießner · 2017
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Shrec’17 track: Point-cloud shape retrieval of non-rigid toys
F. A. Limberger, R. C. Wilson, M. Aono, N. Audebert, A. Boulch, B. Bustos, A. Giachetti, A. Godil, B. Le Saux, B. Li, et al · 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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Foldingnet: Interpretable unsupervised learning on 3d point clouds
Y. Yang, C. Feng, Y. Shen, and D. Tian · 2017
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Active end-effector pose selection for tactile object recognition through monte carlo tree search
M. M. Zhang, N. Atanasov, and K. Daniilidis · 2017
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A survey on multi-task learning
Y. Zhang and Q. Yang · 2017
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Clusternet: Deep hierarchical cluster network with rigorously rotation-invariant representation for point cloud analysis
C. Chen, G. Li, R. Xu, T. Chen, M. Wang, and L. Lin · 2019
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Hypergraph neural networks
Y. Feng, H. You, Z. Zhang, R. Ji, and Y. Gao · 2019
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Relation-shape convolutional neural network for point cloud analysis
Y. Liu, B. Fan, S. Xiang, and C. Pan · 2019
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Kpconv: Flexible and deformable convolution for point clouds
H. Thomas, C. R. Qi, J.-E. Deschaud, B. Marcotegui, F. Goulette, and L. J. Guibas · 2019
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Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data
M. A. Uy, Q.-H. Pham, B.-S. Hua, T. Nguyen, and S.-K. Yeung · 2019
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Dynamic graph cnn for learning on point clouds
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3dmfv: Three-dimensional point cloud classification in real-time using convolutional neural networks
Y. Ben-Shabat, M. Lindenbaum, and A. Fischer · 2018
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Pointwise convolutional neural networks
B.-S. Hua, M.-K. Tran, and S.-K. Yeung · 2018
Cited alongside, same era.
Pointgrid: A deep network for 3d shape understanding
T. Le and Y. Duan · 2018
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Pointcnn: Convolution on x-transformed points
Y. Li, R. Bu, M. Sun, W. Wu, X. Di, and B. Chen · 2018
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Gpatlasrrt: a local tactile exploration planner for recovering the shape of novel objects
C. Rosales, F. Spinelli, M. Gabiccini, C. Zito, and J. L. Wyatt · 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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Learning 3d shape completion under weak supervision
D. Stutz and A. Geiger · 2018
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Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon · 2019
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Endowing deep 3d models with rotation invariance based on principal component analysis
Z. Xiao, H. Lin, R. Li, H. Chao, and S. Ding · 2019
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Linked dynamic graph cnn: Learning on point cloud via linking hierarchical features
K. Zhang, M. Hao, J. Wang, C. W. de Silva, and C. Fu · 2019
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Rotation invariant convolutions for 3d point clouds deep learning
Z. Zhang, B.-S. Hua, D. W. Rosen, and S.-K. Yeung · 2019
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Shellnet: Efficient point cloud convolutional neural networks using concentric shells statistics
Z. Zhang, B.-S. Hua, and S.-K. Yeung · 2019
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3d point capsule networks
Y. Zhao, T. Birdal, H. Deng, and F. Tombari · 2019
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A rotation-invariant framework for deep point cloud analysis
X. Li, R. Li, G. Chen, C.-W. Fu, D. Cohen-Or, and P.-A. Heng · 2020
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Sparse point registration
R. A. Srivatsan, P. Vagdargi, and H. Choset · 2020
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Pointasnl: Robust point clouds processing using nonlocal neural networks with adaptive sampling
X. Yan, C. Zheng, Z. Li, S. Wang, and S. Cui · 2020
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