Point cloud registration method based on 3DMatch network and improved ISS algorithm-Reference-Cited by-同舟云学术

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Point cloud registration method based on 3DMatch network and improved ISS algorithm

Published:2022-02 Issue: Volume: Page:
ISSN:
Container-title:2022 International Symposium on Control Engineering and Robotics (ISCER)
language:
Short-container-title:

Author:

Zhang Zhongda¹,Tang Jialin²,Niu Lihong³,Su Binghua¹,Feng Yi¹,Sheng Yicheng²,Lin Shounan²,Cheng Xipeng¹

Affiliation:

1. Beijing Institute of Technology,Beijing,China

2. Beijing Institute of Technology,Zhuhai,China

3. College of Physics and Optoelectronic Engineering, Shenzhen University,Shenzhen,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9894434/9894435/09894513.pdf?arnumber=9894513

Reference13 articles.

1. Pointnet: Deep learning on point sets for 3d classification and segmentation[C];qi;Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,0

2. SO-Net: Self-Organizing Network for Point Cloud Analysis

3. Rotor surface defect and symmetry detection based on point cloud registration

4. Triple screening point cloud registration method based on image and geometric features

5. Distinctive Image Features from Scale-Invariant Keypoints

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Fast Feature Extraction-Based Method for Globally Accurate Registration of Multi-View Point Clouds;2024 IEEE International Conference on Industrial Technology (ICIT);2024-03-25

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