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Most 3D neural networks are trained from scratch owing to the lack of large-scale labeled 3D datasets.
X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, “Multi-view 3d object detection network for autonomous driving,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition , 2017, pp. 1907–1915
1915
Earlier work this paper cites.
C. Buciluǎ, R. Caruana, and A. Niculescu-Mizil, “Model compression,” in Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining , 2006, pp. 535–541
2006
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition . Ieee, 2009, pp. 248–255
2009
Earlier work this paper cites.
N. Silberman, D. Hoiem, P. Kohli, and R. Fergus, “Indoor segmentation and support inference from rgbd images,” in European conference on computer vision . Springer, 2012, pp. 746–760
2012
Earlier work this paper cites.
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” The International Journal of Robotics Research , vol. 32, no. 11, pp. 1231–1237, 2013
2013
Earlier work this paper cites.
A. Janoch, S. Karayev, Y. Jia, J. T. Barron, M. Fritz, K. Saenko, and T. Darrell, “A category-level 3d object dataset: Putting the kinect to work,” in Consumer depth cameras for computer vision . Springer, 2013, pp. 141–165
2013
Earlier work this paper cites.
J. Xiao, A. Owens, and A. Torralba, “Sun3d: A database of big spaces reconstructed using sfm and object labels,” in Proceedings of the IEEE international conference on computer vision , 2013, pp. 1625–1632
2013
Earlier work this paper cites.
J. Li, R. Zhao, J.-T. Huang, and Y. Gong, “Learning small-size dnn with output-distribution-based criteria,” in Fifteenth annual conference of the international speech communication association , 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
S. Song, S. P. Lichtenberg, and J. Xiao, “Sun rgb-d: A rgb-d scene understanding benchmark suite,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 567–576
2015
Earlier work this paper cites.
H. Su, S. Maji, E. Kalogerakis, and E. Learned-Miller, “Multi-view convolutional neural networks for 3d shape recognition,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 945–953
2015
Earlier work this paper cites.
D. Maturana and S. Scherer, “Voxnet: A 3d convolutional neural network for real-time object recognition,” in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2015, pp. 922–928
2015
Earlier work this paper cites.
C. Doersch, A. Gupta, and A. A. Efros, “Unsupervised visual representation learning by context prediction,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 1422–1430
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 3431–3440
2015
Earlier work this paper cites.
S. Gupta, J. Hoffman, and J. Malik, “Cross modal distillation for supervision transfer,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 2827–2836
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
I. Armeni, O. Sener, A. R. Zamir, H. Jiang, I. Brilakis, M. Fischer, and S. Savarese, “3d semantic parsing of large-scale indoor spaces,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 1534–1543
2016
Earlier work this paper cites.
R. Zhang, P. Isola, and A. A. Efros, “Colorful image colorization,” in European conference on computer vision . Springer, 2016, pp. 649–666
2016
Earlier work this paper cites.
D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, “Context encoders: Feature learning by inpainting,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 2536–2544
2016
Earlier work this paper cites.
M. Noroozi and P. Favaro, “Unsupervised learning of visual representations by solving jigsaw puzzles,” in European Conference on Computer Vision . Springer, 2016, pp. 69–84
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 652–660
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner, “Scannet: Richly-annotated 3d reconstructions of indoor scenes,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 5828–5839
2017
Earlier work this paper cites.
G. Riegler, A. Osman Ulusoy, and A. Geiger, “Octnet: Learning deep 3d representations at high resolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 3577–3586
2017
Earlier work this paper cites.
R. Klokov and V. Lempitsky, “Escape from cells: Deep kd-networks for the recognition of 3d point cloud models,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 863–872
2017
Earlier work this paper cites.
——, “Split-brain autoencoders: Unsupervised learning by cross-channel prediction,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 1058–1067
2017
Cited alongside, same era.
2017
Cited alongside, same era.
J. Yim, D. Joo, J. Bae, and J. Kim, “A gift from knowledge distillation: Fast optimization, network minimization and transfer learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 4133–4141
2017
Cited alongside, same era.
2017
Cited alongside, same era.
P. Bachman, R. D. Hjelm, and W. Buchwalter, “Learning representations by maximizing mutual information across views,” in Advances in Neural Information Processing Systems , 2019, pp. 15 535–15 545
2019
Later among the works it cites.
H.-Y. Chiang, Y.-L. Lin, Y.-C. Liu, and W. H. Hsu, “A unified point-based framework for 3d segmentation,” in 2019 International Conference on 3D Vision (3DV) . IEEE, 2019, pp. 155–163
2019
Later among the works it cites.
H. Thomas, C. R. Qi, J.-E. Deschaud, B. Marcotegui, F. Goulette, and L. J. Guibas, “Kpconv: Flexible and deformable convolution for point clouds,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 6411–6420
2019
Later among the works it cites.
J. Hou, A. Dai, and M. Nießner, “3d-sis: 3d semantic instance segmentation of rgb-d scans,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 4421–4430
2019
Later among the works it cites.
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B. Graham, M. Engelcke, and L. Van Der Maaten, “3d semantic segmentation with submanifold sparse convolutional networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 9224–9232
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Z. Wu, Y. Xiong, S. X. Yu, and D. Lin, “Unsupervised feature learning via non-parametric instance discrimination,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 3733–3742
2018
Cited alongside, same era.
A. Dai and M. Nießner, “3dmv: Joint 3d-multi-view prediction for 3d semantic scene segmentation,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 452–468
2018
Cited alongside, same era.
W. Zeng and T. Gevers, “3dcontextnet: Kd tree guided hierarchical learning of point clouds using local and global contextual cues,” in Proceedings of the European Conference on Computer Vision (ECCV) Workshops , 2018, pp. 0–0
2018
Cited alongside, same era.
S. Xie, S. Liu, Z. Chen, and Z. Tu, “Attentional shapecontextnet for point cloud recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 4606–4615
2018
Cited alongside, same era.
J. Sauder and B. Sievers, “Self-supervised deep learning on point clouds by reconstructing space,” in Advances in Neural Information Processing Systems , 2019, pp. 12 962–12 972
2019
Later among the works it cites.
K. Hassani and M. Haley, “Unsupervised multi-task feature learning on point clouds,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 8160–8171
2019
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S. Ahn, S. X. Hu, A. Damianou, N. D. Lawrence, and Z. Dai, “Variational information distillation for knowledge transfer,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 9163–9171
2019
Later among the works it cites.
W. Park, D. Kim, Y. Lu, and M. Cho, “Relational knowledge distillation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 3967–3976
2019
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B. Peng, X. Jin, J. Liu, D. Li, Y. Wu, Y. Liu, S. Zhou, and Z. Zhang, “Correlation congruence for knowledge distillation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 5007–5016
2019
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2019
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R. Girdhar, D. Tran, L. Torresani, and D. Ramanan, “Distinit: Learning video representations without a single labeled video,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 852–861
2019
Later among the works it cites.
H. Zhang, Y. Li, Y. Jiang, P. Wang, Q. Shen, and C. Shen, “Hyperspectral classification based on lightweight 3-d-cnn with transfer learning,” IEEE Transactions on Geoscience and Remote Sensing , vol. 57, no. 8, pp. 5813–5828, 2019
2019
Later among the works it cites.
C. R. Qi, O. Litany, K. He, and L. J. Guibas, “Deep hough voting for 3d object detection in point clouds,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 9277–9286
2019
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2019
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H. Tang, Z. Liu, S. Zhao, Y. Lin, J. Lin, H. Wang, and S. Han, “Searching efficient 3d architectures with sparse point-voxel convolution,” in European Conference on Computer Vision . Springer, 2020, pp. 685–702
2020
Later among the works it cites.
S. Xie, J. Gu, D. Guo, C. R. Qi, L. Guibas, and O. Litany, “Pointcontrast: Unsupervised pre-training for 3d point cloud understanding,” in European Conference on Computer Vision . Springer, 2020, pp. 574–591
2020
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K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 9729–9738
2020
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2020
Later among the works it cites.
O. Henaff, “Data-efficient image recognition with contrastive predictive coding,” in International Conference on Machine Learning . PMLR, 2020, pp. 4182–4192
2020
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I. Misra and L. v. d. Maaten, “Self-supervised learning of pretext-invariant representations,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 6707–6717
2020
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A. Kundu, X. Yin, A. Fathi, D. Ross, B. Brewington, T. Funkhouser, and C. Pantofaru, “Virtual multi-view fusion for 3d semantic segmentation,” in European Conference on Computer Vision . Springer, 2020, pp. 518–535
2020
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2020
Later among the works it cites.
C. Chen, S. Qian, Q. Fang, and C. Xu, “Hapgn: Hierarchical attentive pooling graph network for point cloud segmentation,” IEEE Transactions on Multimedia , pp. 1–1, 2020
2020
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C. R. Qi, X. Chen, O. Litany, and L. J. Guibas, “Imvotenet: Boosting 3d object detection in point clouds with image votes,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 4404–4413
2020
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Y. Rao, J. Lu, and J. Zhou, “Global-local bidirectional reasoning for unsupervised representation learning of 3d point clouds,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 5376–5385
2020
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A. Newell and J. Deng, “How useful is self-supervised pretraining for visual tasks?” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 7345–7354
2020
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2020
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I. Merino, J. Azpiazu, A. Remazeilles, and B. Sierra, “3d convolutional neural networks initialized from pretrained 2d convolutional neural networks for classification of industrial parts,” Sensors , vol. 21, no. 4, p. 1078, 2021
2021
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