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Autonomous vehicles need to have a semantic understanding of the three-dimensional world around them in order to reason about their environment.
P. Jaccard, “Etude de la distribution florale dans une portion des alpes et du jura,” Bulletin de la Societe Vaudoise des Sciences Naturelles , vol. 37, pp. 547–579, 01 1901
1901
Earlier work this paper cites.
1945
Earlier work this paper cites.
T. Sorensen, “A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on danish commons,” Biologiske Skrifter , no. 5, pp. 1–34, 1948
1948
Earlier work this paper cites.
I. J. Good, “Some terminology and notation in information theory,” Proceedings of the IEE - Part C: Monographs , vol. 103, no. 3, pp. 200–204, March 1956
1956
Earlier work this paper cites.
2010
Earlier work this paper cites.
P. Krähenbühl and V. Koltun, “Efficient inference in fully connected crfs with gaussian edge potentials,” in Advances in Neural Information Processing Systems 24 , J. Shawe-Taylor, R. S. Zemel, P. L. Bartlett, F. Pereira, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2011, pp. 109–117
2011
Earlier work this paper cites.
A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite,” in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) , 2012, pp. 3354–3361
2012
Earlier work this paper cites.
G. Huang, H. Lee, and E. Learned-Miller, “Learning hierarchical representations for face verification with convolutional deep belief networks,” in Proceedings IEEE Computer Society Conference on Computer Vision and Pattern Recognition , 06 2012, pp. 2518–2525
2012
Earlier work this paper cites.
Y. Taigman et al. , “DeepFace: Closing the gap to human-level performance in face verification,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2014, pp. 1701–1708
2014
Earlier work this paper cites.
F. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,” in ICLR , 2016
2016
Earlier work this paper cites.
B. Wu et al. , “Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud,” 2018 IEEE International Conference on Robotics and Automation (ICRA) , pp. 1887–1893, 2017
2017
Earlier work this paper cites.
F. J. Lawin et al. , “Deep projective 3d semantic segmentation,” Proceedings of International Conference on Computer Analysis of Images and Patterns , 2017
2017
Earlier work this paper cites.
A. Boulch, B. L. Saux, and N. Audebert, “Unstructured point cloud semantic labeling using deep segmentation networks,” in Proceedings of the Workshop on 3D Object Retrieval , ser. 3Dor ’17. Eurographics Association, 2017, p. 17–24
2017
Cited alongside, same era.
C. R. Qi et al. , “PointNet: Deep learning on point sets for 3d classification and segmentation,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , July 2017, pp. 77–85
2017
Cited alongside, same era.
——, “PointNet++: Deep hierarchical feature learning on point sets in a metric space,” NeurIPS , 2017
2017
Cited alongside, same era.
E. Shelhamer, J. Long, and T. Darrell, “Fully convolutional networks for semantic segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 39, no. 4, pp. 640–651, Apr. 2017
2017
Cited alongside, same era.
S. Wang et al. , “Deep parametric continuous convolutional neural networks,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2018
2018
Later among the works it cites.
L. Landrieu and M. Simonovsky, “Large-scale point cloud semantic segmentation with superpoint graphs,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , June 2018, pp. 4558–4567
2018
Later among the works it cites.
A. Milioto et al. , “RangeNet++: Fast and Accurate LiDAR Semantic Segmentation,” in IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS) , 2019
2019
Later among the works it cites.
J. Behley et al. , “SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences,” in Proc. of the IEEE/CVF International Conf. on Computer Vision (ICCV) , 2019
2019
Later among the works it cites.
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2017
Cited alongside, same era.
V. Badrinarayanan, A. Kendall, and R. Cipolla, “Segnet: A deep convolutional encoder-decoder architecture for image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2017
2017
Cited alongside, same era.
F. Piewak et al. , “Boosting lidar-based semantic labeling by cross-modal training data generation,” in ECCV Workshops , 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
D. Rethage et al. , “Fully-convolutional point networks for large-scale point clouds,” in European Conference on Computer Vision (ECCV) . Springer International Publishing, 2018, pp. 625–640
2018
Cited alongside, same era.
B. Graham, M. Engelcke, and L. v. d. Maaten, “3d semantic segmentation with submanifold sparse convolutional networks,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , June 2018, pp. 9224–9232
2018
Cited alongside, same era.
B.-S. Hua, M.-K. Tran, and S.-K. Yeung, “Pointwise convolutional neural networks,” in Computer Vision and Pattern Recognition (CVPR) , 2018
2018
Cited alongside, same era.
2019
Later among the works it cites.
B. Wu et al. , “Squeezesegv2: Improved model structure and unsupervised domain adaptation for road-object segmentation from a lidar point cloud,” 2019 IEEE International Conference on Robotics and Automation (ICRA) , pp. 4379–4382, 2019
2019
Later among the works it cites.
H. Zhao et al. , “PointWeb: Enhancing local neighborhood features for point cloud processing,” 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
L. Wang et al. , “Graph attention convolution for point cloud semantic segmentation,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
G. Lin et al. , “Refinenet: Multi-path refinement networks for dense prediction,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2019
2019
Later among the works it cites.