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LiDAR semantic segmentation essential for advanced autonomous driving is required to be accurate, fast, and easy-deployed on mobile platforms.
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.
H. Noh, S. Hong, and B. Han, “Learning deconvolution network for semantic segmentation,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 1520–1528
2015
Earlier work this paper cites.
H. Vanholder, “Efficient inference with tensorrt,” 2016
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.
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,” in Advances in Neural Information Processing Systems , vol. 30, 2017, pp. 5105–5114
2017
Earlier work this paper cites.
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid scene parsing network,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2881–2890
2017
Earlier work this paper cites.
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” IEEE transactions on pattern analysis and machine intelligence , vol. 40, no. 4, pp. 834–848, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He, “Aggregated residual transformations for deep neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 1492–1500
2017
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, pp. 820–830, 2018
2018
Earlier work this paper cites.
L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 801–818
2018
Cited alongside, same era.
2018
Cited alongside, same era.
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
Cited alongside, same era.
A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, “Pointpillars: Fast encoders for object detection from point clouds,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 12 697–12 705
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.
T. Cortinhal, G. Tzelepis, and E. E. Aksoy, “Salsanext: Fast, uncertainty-aware semantic segmentation of lidar point clouds,” in International Symposium on Visual Computing . Springer, 2020, pp. 207–222
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
Later among the works it cites.
2020
Later among the works it cites.
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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.
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. 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. 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.
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
Cited alongside, same era.
2020
Cited alongside, same era.
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
Cited alongside, same era.
H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 621–11 631
2020
Later among the works it cites.
Y. A. Alnaggar, M. Afifi, K. Amer, and M. ElHelw, “Multi projection fusion for real-time semantic segmentation of 3d lidar point clouds,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2021, pp. 1800–1809
2021
Later among the works it cites.
X. Zhu, H. Zhou, T. Wang, F. Hong, Y. Ma, W. Li, H. Li, and D. Lin, “Cylindrical and asymmetrical 3d convolution networks for lidar segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 9939–9948
2021
Later among the works it cites.
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
Later among the works it cites.
J. Xu, R. Zhang, J. Dou, Y. Zhu, J. Sun, and S. Pu, “Rpvnet: A deep and efficient range-point-voxel fusion network for lidar point cloud segmentation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 16 024–16 033
2021
Later among the works it cites.
R. Razani, R. Cheng, E. Taghavi, and L. Bingbing, “Lite-hdseg: Lidar semantic segmentation using lite harmonic dense convolutions,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) , 2021, pp. 9550–9556
2021
Later among the works it cites.
M. Ye, S. Xu, T. Cao, and Q. Chen, “Drinet: A dual-representation iterative learning network for point cloud segmentation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 7447–7456
2021
Later among the works it cites.