BEACon: a boundary embedded attentional convolution network for point cloud instance segmentation

Author:

Liu TianruiORCID,Cai Yiyu,Zheng Jianmin,Thalmann Nadia Magnenat

Funder

National Research Foundation Singapore

Publisher

Springer Science and Business Media LLC

Subject

Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software

Reference43 articles.

1. Haoran, L., Fazhi, H., Yilin, C.: Learning dynamic simultaneous clustering and classification via automatic differential evolution and firework algorithm. Appl. Soft Comput. J. 96, 106593 (2020)

2. Zhang, S., He, F.: DRCDN: learning deep residual convolutional dehazing networks. Vis. Comput. 36, 1797–1808 (2020)

3. Li, Y., Bu, R., Sun, M., Chen, B.: PointCNN: Convolution On X-Transformed Points. In: Advances in Neural Information Processing Systems (2018)

4. Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: Deep hierarchical feature learning on point sets in a metric space. In: Advances in Neural Information Processing Systems (2017)

5. Rethage, D., Wald, J., Sturm, J, Navab, N., Tombari, F.: Fully-convolutional point networks for large-scale point clouds. In: ECCV (2018)

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