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In this paper, we address semantic segmentation of road-objects from 3D LiDAR point clouds.
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2008
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2009
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2015
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S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C. Huang, and P. H. Torr, “Conditional random fields as recurrent neural networks,” in
2015
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D. Maturana and S. Scherer, “3d convolutional neural networks for landing zone detection from lidar,” in
2015
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2015
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2016
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B. Li, T. Zhang, and T. Xia, “Vehicle detection from 3d lidar using fully convolutional network,”
2016
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S. R. Richter, V. Vineet, S. Roth, and V. Koltun, “Playing for data: Ground truth from computer games,” in
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
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Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2017
Closest in time.
M.-O. Shin, G.-M. Oh, S.-W. Kim, and S.-W. Seo, “Real-time and accurate segmentation of 3-d point clouds based on gaussian process regression,”
2017
Closest in time.
L. Caltagirone, S. Scheidegger, L. Svensson, and M. Wahde, “Fast lidar-based road detection using fully convolutional neural networks.” in
2017
Closest in time.