Opto-Electronic Engineering, Volume. 50, Issue 2, 220148(2023)
3D laser point cloud clustering method based on image information constraints
[3] [3] Su H, Maji S, Kalogerakis E, et al. Multi-view convolutional neural networks for 3D shape recognition[C]//2015 IEEE International Conference on Computer Vision, 2015: 945–953. https://doi.org/10.1109/ICCV.2015.114.
[4] [4] Maturana D, Scherer S. VoxNet: a 3D convolutional neural network for real-time object recognition[C]//2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015: 922–928. https://doi.org/10.1109/IROS.2015.7353481.
[5] [5] Qi C R, Su H, Mo K C, et al. PointNet: deep learning on point sets for 3D classification and segmentation[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017: 77–85. https://doi.org/10.1109/CVPR.2017.16.
[6] [6] Qi C R, Yi L, Su H, et al. PointNet++: deep hierarchical feature learning on point sets in a metric space[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017: 5105–5114.
Get Citation
Copy Citation Text
Jinze Xia, Haoming Sun, Shenghui Hu, Dongtai Liang. 3D laser point cloud clustering method based on image information constraints[J]. Opto-Electronic Engineering, 2023, 50(2): 220148
Category: Article
Received: Jun. 30, 2022
Accepted: Nov. 28, 2022
Published Online: Apr. 13, 2023
The Author Email: Liang Dongtai (liangdongtai@nbu.edu.cn)