RegiFormer: Unsupervised Point Cloud Registration via Geometric Local-to-Global Transformer and Self-Augmentation-Reference-Cited by-同舟云学术

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RegiFormer: Unsupervised Point Cloud Registration via Geometric Local-to-Global Transformer and Self-Augmentation

Published:2024 Issue: Volume:62 Page:1-13
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Zheng Chengyu¹^ORCID,Ma Mengjiao¹,Chen Zhilei¹^ORCID,Chen Honghua¹^ORCID,Wang Weiming²^ORCID,Wei Mingqiang¹^ORCID

Affiliation:

1. School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

2. School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, SAR, China

Funder

Research Grants Council of the Hong Kong Special Administrative Region, China

National Natural Science Foundation of China

Shenzhen Science and Technology Program

Basic and Applied Basic Research Foundation of Guangdong Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/36/10354519/10613860.pdf?arnumber=10613860

Reference63 articles.

1. Reliable Inlier Evaluation for Unsupervised Point Cloud Registration

2. Building Rome in a day

3. IMLS-SLAM: Scan-to-Model Matching Based on 3D Data

4. Model globally, match locally: Efficient and robust 3D object recognition

5. A method for registration of 3-D shapes

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