DFAMNet: dual fusion attention multi-modal network for semantic segmentation on LiDAR point clouds-Reference-Cited by-同舟云学术

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DFAMNet: dual fusion attention multi-modal network for semantic segmentation on LiDAR point clouds

Published:2024-02 Issue:4 Volume:54 Page:3169-3180
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Li Mingjie,Wang Gaihua,Zhu Minghao,Li Chunzheng,Liu Hong,Pan Xuran,Long Qian

Funder

the National Nature Science Fund of China

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10489-024-05302-7.pdf

Reference46 articles.

1. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440

2. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. Springer, pp 234–241

3. Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40:834–848

4. He K, Gkioxari G, Dollár P, Girshick R (2017) Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 2961–2969

5. Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2881–2890

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1. RPV-CASNet: range-point-voxel integration with channel self-attention network for lidar point cloud segmentation;Applied Intelligence;2024-06-14

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