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Semantic segmentation is an important component in the perception systems of autonomous vehicles.
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The Cityscapes dataset for semantic urban scene understanding. In: CVPR (2016)
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Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. In: ICLR (2017)
2017
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Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: CVPR. pp. 2881–2890 (2017)
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Mahajan, D., Girshick, R., Ramanathan, V., He, K., Paluri, M., Li, Y., Bharambe, A., van der Maaten, L.: Exploring the limits of weakly supervised pretraining. In: ECCV. pp. 181–196 (2018)
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Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., Gall, J.: SemanticKITTI: A dataset for semantic scene understanding of LiDAR sequences. In: ICCV (2019)
2019
Cited alongside, same era.
Milioto, A., Vizzo, I., Behley, J., Stachniss, C.: RangeNet++: Fast and accurate LiDAR semantic segmentation. In: IROS. pp. 4213–4220. IEEE (2019)
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Thomas, H., Qi, C.R., Deschaud, J.E., Marcotegui, B., Goulette, F., Guibas, L.J.: KPConv: Flexible and deformable convolution for point clouds. In: ICCV (2019)
2019
Cited alongside, same era.
Cheng, B., Collins, M.D., Zhu, Y., Liu, T., Huang, T.S., Adam, H., Chen, L.C.: Panoptic-deeplab: A simple, strong, and fast baseline for bottom-up panoptic segmentation. In: CVPR. pp. 12475–12485 (2020)
2020
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Cortinhal, T., Tzelepis, G., Aksoy, E.E.: SalsaNext: Fast, uncertainty-aware semantic segmentationof LiDAR point clouds for autonomous driving. Arxiv (2020)
2020
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Hu, Q., Markham, A., Rosa, S., Trigoni, N., Wang, Z., Xie, L., Yang, B.: Randla- net: Efficient semantic segmentation of large- scale point clouds. In: CVPR (2020)
2020
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2020
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2019
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2020
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