Enhanced Iterative Closest Point Algorithm Based on an Improved Northern Goshawk Optimization Algorithm and Random Sampling-Reference-Cited by-同舟云学术

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Enhanced Iterative Closest Point Algorithm Based on an Improved Northern Goshawk Optimization Algorithm and Random Sampling

Published:2024-04-26 Issue: Volume:8 Page:1-5
ISSN:
Container-title:Proceedings of the 2024 2nd Asia Conference on Computer Vision, Image Processing and Pattern Recognition
language:
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Author:

Li Ke¹^ORCID,Fu Shengwei¹^ORCID,Huang Haisong¹^ORCID,Fan Qingsong¹^ORCID

Affiliation:

1. Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, China

Funder

National Natural Science Foundation of China

Guizhou Provincial Science and Technology Projects

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3663976.3664025

Reference14 articles.

1. Registration of large-scale terrestrial laser scanner point clouds: A review and benchmark

2. Automated registration of multi-view point clouds using sphere targets

3. A method for registration of 3-D shapes

4. L. Zheng C. Mai W. Liao Y. Wen G. Liu 3D point cloud registration for apple tree based on Kinect camera 47 (2016) 9-14.

5. Robust Euclidean alignment of 3D point sets: the trimmed iterative closest point algorithm

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