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In this paper, we introduce a deep encoder-decoder network, named SalsaNet, for efficient semantic segmentation of 3D LiDAR point clouds.
1902
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1902
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A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 2012
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2014
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O. Ronneberger, P.Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention (MICCAI) , ser. LNCS, vol. 9351. Springer, 2015, pp. 234–241
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2015
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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
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E. Shelhamer, J. Long, and T. Darrell, “Fully convolutional networks for semantic segmentation.” PAMI , 2016
2016
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2016
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2017
Cited alongside, same era.
L. Caltagirone, S. Scheidegger, L. Svensson, and M. Wahde, “Fast lidar-based road detection using fully convolutional neural networks,” in IEEE Intelligent Vehicles Symposium , 2017, pp. 1019–1024
2017
Cited alongside, same era.
2017
Cited alongside, same era.
D. Zermas, I. Izzat, and N. Papanikolopoulos, “Fast segmentation of 3d point clouds: A paradigm on lidar data for autonomous vehicle applications,” in 2017 IEEE International Conference on Robotics and Automation (ICRA) , May 2017, pp. 5067–5073
2017
Cited alongside, same era.
2018
Later among the works it cites.
C. Zhang, W. Luo, and R. Urtasun, “Efficient convolutions for real-time semantic segmentation of 3d point clouds,” in Proceedings of the International Conference on 3D Vision (3DV) , 2018
2018
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Y. Zhou and O. Tuzel, “Voxelnet: End-to-end learning for point cloud based 3d object detection,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , June 2018, pp. 4490–4499
2018
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Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li, and N. Sun, “Rt3d: Real-time 3-d vehicle detection in lidar point cloud for autonomous driving,” IEEE Robotics and Automation Letters , vol. 3, no. 4, pp. 3434–3440, Oct 2018
2018
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L. Chen, J. Yang, and H. Kong, “Lidar-histogram for fast road and obstacle detection,” in 2017 IEEE International Conference on Robotics and Automation (ICRA) , May 2017, pp. 1343–1348
2017
Cited alongside, same era.
M. Velas, M. Spanel, M. Hradis, and A. Herout, “Cnn for very fast ground segmentation in velodyne lidar data,” in IEEE Int. Conf. on Autonomous Robot Systems and Competitions , 2018, pp. 97–103
2018
Cited alongside, same era.
B. Wu, A. Wan, X. Yue, and K. Keutzer, “Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud,” ICRA , 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
M. Teichmann, M. Weber, M. Zöllner, R. Cipolla, and R. Urtasun, “Multinet: Real-time joint semantic reasoning for autonomous driving,” in IEEE Intelligent Vehicles Symposium , 2018, pp. 1013–1020
2018
Cited alongside, same era.
M. Simon, S. Milz, K. Amende, and H. Gross, “Complex-yolo: Real-time 3d object detection on point clouds,” CoRR , 2018
2018
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2018
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2019
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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 ICRA , 2019
2019
Closest in time.