Point Cloud Registration Network Based on Convolution Fusion and Attention Mechanism-Reference-Cited by-同舟云学术

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Point Cloud Registration Network Based on Convolution Fusion and Attention Mechanism

Published:2023-11-02 Issue:9 Volume:55 Page:12625-12645
ISSN:1370-4621
Container-title:Neural Processing Letters
language:en
Short-container-title:Neural Process Lett

Author:

Zhu Wei,Ying Yue,Zhang Jin,Wang Xiuli,Zheng Yayu

Funder

Zhejiang Provincial Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software

Link

https://link.springer.com/content/pdf/10.1007/s11063-023-11435-6.pdf

Reference63 articles.

1. Huang X, Mei G, Zhang J, Abbas R (2021) A comprehensive survey on point cloud registration. arXiv preprint arXiv:2103.02690

2. Takimoto R Y, Tsuzuki MdSG, Vogelaar R, Castro Martins T, Sato A K, Iwao Y, Gotoh T, Kagei S (2016) 3d reconstruction and multiple point cloud registration using a low precision RGB-D sensor. Mechatronics 35:11–22

3. Dang Z, Wang L, Guo Y, Salzmann M (2022) Learning-based point cloud registration for 6d object pose estimation in the real world. In: European conference on computer vision, pp. 19– 37 . Springer

4. Choy C, Park J, Koltun V (2019) Fully convolutional geometric features. In: Proceedings of the IEEE/CVF international conference on computer vision, pp. 8958– 8966

5. Zeng A, Song S, Nießner M, Fisher M, Xiao J, Funkhouser T (2017) 3dmatch: learning local geometric descriptors from RGB-D reconstructions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1802– 1811

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