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Attention and Mamba Based Iterative Registration Network for Low-Overlap and Large-Scale Point Cloud

Haotian Cao1,2, Qingsheng Zhu1,2,3,*
1 School of Astronomy and Space Science, University of Science and Technology of China, Hefei, China
2 Nanjing Astronomical Instruments Research Center, Chinese Academy of Sciences, Nanjing, China
3 CAS Nanjing Astronomical Instruments Co., Ltd., Nanjing, China
* Corresponding Author: Qingsheng Zhu. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.081695

Received 06 March 2026; Accepted 27 April 2026; Published online 18 May 2026

Abstract

Point Cloud Registration (PCR) is a basic task in computer vision, mobile robotics, and autonomous driving. PCR primarily faces challenges, including insufficient registration performance in low-overlap scenarios and high computational resource consumption in large-scale point cloud scenarios. Most recent PCR methods are transformer-based. Methods like transformers have quadratic computational complexity

Keywords

Point cloud registration; deep learning; computer vision; attention mechanism; mamba model; iterative network
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