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Autonomous vehicles were experiencing rapid development in the past few years.
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2017
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2017
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2017
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2017
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J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara, A. Fathi, I. Fischer, Z. Wojna, Y. Song, S. Guadarrama, and et al., “Speed/accuracy trade-offs for modern convolutional object detectors,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , Jul 2017. [Online]. Available: http://dx.doi.org/10.1109/CVPR.2017.351
2017
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2017
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Y. Zhou and O. Tuzel, “Voxelnet: End-to-end learning for point cloud based 3d object detection,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , Jun 2018. [Online]. Available: http://dx.doi.org/10.1109/cvpr.2018.00472
2018
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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) , 2018, pp. 0–0
2018
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B.-S. Hua, M.-K. Tran, and S.-K. Yeung, “Pointwise convolutional neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 984–993
2018
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M. Atzmon, H. Maron, and Y. Lipman, “Point convolutional neural networks by extension operators,” ACM Transactions on Graphics , vol. 37, no. 4, p. 1–12, Aug 2018. [Online]. Available: http://dx.doi.org/10.1145/3197517.3201301
2018
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Y. Li, R. Bu, M. Sun, W. Wu, X. Di, and B. Chen, “Pointcnn: Convolution on x-transformed points,” in Advances in Neural Information Processing Systems 31 , S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, Eds. Curran Associates, Inc., 2018, pp. 820–830. [Online]. Available: http://papers.nips.cc/paper/7362-pointcnn-convolution-on-x-transformed-points.pdf
2018
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F. Ma and S. Karaman, “Sparse-to-dense: Depth prediction from sparse depth samples and a single image,” 2018 IEEE International Conference on Robotics and Automation (ICRA) , May 2018. [Online]. Available: http://dx.doi.org/10.1109/ICRA.2018.8460184
2018
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2019
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W. Wu, Z. Qi, and L. Fuxin, “Pointconv: Deep convolutional networks on 3d point clouds,” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , Jun 2019. [Online]. Available: http://dx.doi.org/10.1109/CVPR.2019.00985
2019
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2019
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2019
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2020
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K. Park, S. Kim, and K. Sohn, “High-precision depth estimation using uncalibrated lidar and stereo fusion,” IEEE Transactions on Intelligent Transportation Systems , vol. 21, no. 1, pp. 321–335, Jan 2020
2020
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2020
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2020
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2020
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2020
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2020
Closest in time.
X. Cheng, P. Wang, G. Chenye, and R. Yang, “Cspn++: Learning context and resource aware convolutional spatial propagation networks for depth completion,” Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20) , 2020
2020
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J. Lee and T. Park, “Fast road detection by cnn-based camera-lidar fusion and spherical coordinate transformation,” IEEE Transactions on Intelligent Transportation Systems , pp. 1–9, 2020
2020
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H. Guan, Y. Yu, D. Peng, Y. Zang, J. Lu, A. Li, and J. Li, “A convolutional capsule network for traffic-sign recognition using mobile lidar data with digital images,” IEEE Geoscience and Remote Sensing Letters , vol. 17, no. 6, pp. 1067–1071, 2020
2020
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J. Luiten, T. Fischer, and B. Leibe, “Track to reconstruct and reconstruct to track,” IEEE Robotics and Automation Letters , vol. 5, no. 2, p. 1803–1810, Apr 2020. [Online]. Available: http://dx.doi.org/10.1109/LRA.2020.2969183
2020
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A. R. Dhamija, M. Günther, J. Ventura, and T. E. Boult, “The overlooked elephant of object detection: Open set,” in 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) , 2020, pp. 1010–1019
2020
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K. Wong, S. Wang, M. Ren, M. Liang, and R. Urtasun, “Identifying unknown instances for autonomous driving,” in Conference on Robot Learning , 2020, pp. 384–393
2020
Closest in time.