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In recent years, with the development of computing resources and LiDAR, point cloud semantic segmentation has attracted many researchers.
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.
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.
C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
F. J. Lawin, M. Danelljan, P. Tosteberg, G. Bhat, F. S. Khan, and M. Felsberg, “Deep projective 3d semantic segmentation,” in International Conference on Computer Analysis of Images and Patterns . Springer, 2017, pp. 95–107
2017
Earlier work this paper cites.
L. Tchapmi, C. Choy, I. Armeni, J. Gwak, and S. Savarese, “Segcloud: Semantic segmentation of 3d point clouds,” in 2017 international conference on 3D vision (3DV) . IEEE, 2017, pp. 537–547
2017
Earlier work this paper cites.
M. Tatarchenko, J. Park, V. Koltun, and Q.-Y. Zhou, “Tangent convolutions for dense prediction in 3d,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 3887–3896
2018
Earlier work this paper cites.
D. Rethage, J. Wald, J. Sturm, N. Navab, and F. Tombari, “Fully-convolutional point networks for large-scale point clouds,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 596–611
2018
Earlier work this paper cites.
Y. Yan, Y. Mao, and B. Li, “Second: Sparsely embedded convolutional detection,” Sensors , vol. 18, no. 10, p. 3337, 2018
2018
Earlier work this paper cites.
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
Earlier work this paper cites.
M. Berman, A. R. Triki, and M. B. Blaschko, “The lovász-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 4413–4421
2018
Earlier work this paper cites.
Y. Li, R. Bu, M. Sun, W. Wu, X. Di, and B. Chen, “Pointcnn: Convolution on x-transformed points,” Advances in neural information processing systems , vol. 31, 2018
2018
Earlier work this paper cites.
X. Ye, J. Li, H. Huang, L. Du, and X. Zhang, “3d recurrent neural networks with context fusion for point cloud semantic segmentation,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 403–417
2018
Earlier work this paper cites.
L. Landrieu and M. Simonovsky, “Large-scale point cloud semantic segmentation with superpoint graphs,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 4558–4567
2018
Cited alongside, same era.
S. Wang, S. Suo, W.-C. Ma, A. Pokrovsky, and R. Urtasun, “Deep parametric continuous convolutional neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 2589–2597
2018
Cited alongside, same era.
J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, C. Stachniss, and J. Gall, “Semantickitti: A dataset for semantic scene understanding of lidar sequences,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 9297–9307
2019
Cited alongside, same era.
H. Zhao, L. Jiang, C.-W. Fu, and J. Jia, “Pointweb: Enhancing local neighborhood features for point cloud processing,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 5565–5573
Y. Guo, H. Wang, Q. Hu, H. Liu, L. Liu, and M. Bennamoun, “Deep learning for 3d point clouds: A survey,” IEEE transactions on pattern analysis and machine intelligence , vol. 43, no. 12, pp. 4338–4364, 2020
2020
Later among the works it cites.
H. Lin, Z. Luo, W. Li, Y. Chen, C. Wang, and J. Li, “Adaptive pyramid context fusion for point cloud perception,” IEEE Geoscience and Remote Sensing Letters , 2020
2020
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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.
Q. Hu, B. Yang, L. Xie, S. Rosa, Y. Guo, Z. Wang, N. Trigoni, and A. Markham, “Randla-net: Efficient semantic segmentation of large-scale point clouds,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 108–11 117
2020
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2019
Cited alongside, same era.
Z. Zhang, B.-S. Hua, and S.-K. Yeung, “Shellnet: Efficient point cloud convolutional neural networks using concentric shells statistics,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 1607–1616
2019
Cited alongside, same era.
A. Milioto, I. Vizzo, J. Behley, and C. Stachniss, “Rangenet++: Fast and accurate lidar semantic segmentation,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2019, pp. 4213–4220
2019
Cited alongside, same era.
B. Wu, X. Zhou, S. Zhao, X. Yue, and K. Keutzer, “Squeezesegv2: Improved model structure and unsupervised domain adaptation for road-object segmentation from a lidar point cloud,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 4376–4382
2019
Cited alongside, same era.
C. Choy, J. Gwak, and S. Savarese, “4d spatio-temporal convnets: Minkowski convolutional neural networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 3075–3084
2019
Cited alongside, same era.
Z. Liu, H. Tang, Y. Lin, and S. Han, “Point-voxel cnn for efficient 3d deep learning,” in Advances in Neural Information Processing Systems , 2019
2019
Cited alongside, same era.
H. Fang and F. Lafarge, “Pyramid scene parsing network in 3d: Improving semantic segmentation of point clouds with multi-scale contextual information,” Isprs journal of photogrammetry and remote sensing , vol. 154, pp. 246–258, 2019
2019
Cited alongside, same era.
X. Li, W. Wang, X. Hu, and J. Yang, “Selective kernel networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 510–519
2019
Cited alongside, same era.
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/CVF international conference on computer vision , 2019, pp. 6411–6420
2019
Cited alongside, same era.
C. Xu, B. Wu, Z. Wang, W. Zhan, P. Vajda, K. Keutzer, and M. Tomizuka, “Squeezesegv3: Spatially-adaptive convolution for efficient point-cloud segmentation,” in European Conference on Computer Vision . Springer, 2020, pp. 1–19
2020
Later among the works it cites.
E. E. Aksoy, S. Baci, and S. Cavdar, “Salsanet: Fast road and vehicle segmentation in lidar point clouds for autonomous driving,” in 2020 IEEE intelligent vehicles symposium (IV) . IEEE, 2020, pp. 926–932
2020
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Y. Zhang, Z. Zhou, P. David, X. Yue, Z. Xi, B. Gong, and H. Foroosh, “Polarnet: An improved grid representation for online lidar point clouds semantic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 9601–9610
2020
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F. Zhang, J. Fang, B. Wah, and P. Torr, “Deep fusionnet for point cloud semantic segmentation,” in European Conference on Computer Vision . Springer, 2020, pp. 644–663
2020
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2020
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R. Cheng, R. Razani, E. Taghavi, E. Li, and B. Liu, “2-s3net: Attentive feature fusion with adaptive feature selection for sparse semantic segmentation network,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 12 547–12 556
2021
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M. Gerdzhev, R. Razani, E. Taghavi, and L. Bingbing, “Tornado-net: multiview total variation semantic segmentation with diamond inception module,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 9543–9549
2021
Later among the works it cites.