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Earlier work demonstrates the promise of deep-learning-based approaches for point cloud segmentation; however, these approaches need to be improved to be practically useful.
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2016
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2016
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2016
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D. Zermas, I. Izzat, and N. Papanikolopoulos, “Fast segmentation of 3d point clouds: A paradigm on lidar data for autonomous vehicle applications,” in
2017
Cited alongside, same era.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in
2017
Cited alongside, same era.
C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” in
2017
Cited alongside, same era.
2017
Cited alongside, same era.
G. Csurka, “Domain adaptation for visual applications: A comprehensive survey,”
2017
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,” in
2018
Closest in time.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,”
2018
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P. Morerio, J. Cavazza, and V. Murino, “Minimal-entropy correlation alignment for unsupervised deep domain adaptation,” in
2018
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2018
Closest in time.
J. Hoffman, E. Tzeng, T. Park, J.-Y. Zhu, P. Isola, K. Saenko, A. A. Efros, and T. Darrell, “Cycada: Cycle-consistent adversarial domain adaptation,” in
2018
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Cited alongside, same era.
B. Sun, J. Feng, and K. Saenko, “Correlation alignment for unsupervised domain adaptation,” in
2017
Cited alongside, same era.
J. Zhuo, S. Wang, W. Zhang, and Q. Huang, “Deep unsupervised convolutional domain adaptation,” in
2017
Cited alongside, same era.
E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell, “Adversarial discriminative domain adaptation,” in
2017
Cited alongside, same era.
A. Shrivastava, T. Pfister, O. Tuzel, J. Susskind, W. Wang, and R. Webb, “Learning from simulated and unsupervised images through adversarial training,” in
2017
Cited alongside, same era.
K. Bousmalis, N. Silberman, D. Dohan, D. Erhan, and D. Krishnan, “Unsupervised pixel-level domain adaptation with generative adversarial networks,” in
2017
Cited alongside, same era.
M. Johnson-Roberson, C. Barto, R. Mehta, S. N. Sridhar, K. Rosaen, and R. Vasudevan, “Driving in the matrix: Can virtual worlds replace human-generated annotations for real world tasks?” in
2017
Cited alongside, same era.
S. R. Richter, Z. Hayder, and V. Koltun, “Playing for benchmarks,” in
2017
Cited alongside, same era.
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X. Yue, B. Wu, S. A. Seshia, K. Keutzer, and A. L. Sangiovanni-Vincentelli, “A lidar point cloud generator: from a virtual world to autonomous driving,” in
2018
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P. Krähenbühl, “Free supervision from video games,” in
2018
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J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in
2018
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Y. Li, N. Wang, J. Shi, X. Hou, and J. Liu, “Adaptive batch normalization for practical domain adaptation,”
2018
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2018
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Y. Zhang, P. David, and B. Gong, “Curriculum domain adaptation for semantic segmentation of urban scenes,” in
2049
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