A training dataset for semantic segmentation of urban point cloud map for intelligent vehicles

Author:

Song Hamin,Jo Kichun,Cho Jieun,Son Youngrok,Kim Chansoo,Han Kwangjin

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Computer Science Applications,Engineering (miscellaneous),Atomic and Molecular Physics, and Optics

Reference40 articles.

1. Armeni, I., Sener, O., Zamir, A.R., Jiang, H., Brilakis, I., Fischer, M. and Savarese, S., 2016. 3d semantic parsing of large-scale indoor spaces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1534–1543.

2. Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C. and Gall, J., 2019. Semantickitti: A dataset for semantic scene understanding of lidar sequences. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9297–9307.

3. Convpoint: Continuous convolutions for point cloud processing;Boulch;Computers & Graphics,2020

4. Chang, A.X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H. et al., 2015. Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012.

5. Chen, L.-Z., Li, X.-Y., Fan, D.-P., Wang, K., Lu, S.-P. and Cheng, M.-M., 2019. Lsanet: Feature learning on point sets by local spatial aware layer. arXiv preprint arXiv:1905.05442.

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