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Decoupled deep hough voting for point cloud registration

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Abstract

Estimating rigid transformation using noisy correspondences is critical to feature-based point cloud registration. Recently, a series of studies have attempted to combine traditional robust model fitting with deep learning. Among them, DHVR proposed a hough voting-based method, achieving new state-of-the-art performance. However, we find voting on rotation and translation simultaneously hinders achieving better performance. Therefore, we proposed a new hough voting-based method, which decouples rotation and translation space. Specifically, we first utilize hough voting and a neural network to estimate rotation. Then based on good initialization on rotation, we can easily obtain accurate rigid transformation. Extensive experiments on 3DMatch and 3DLoMatch datasets show that our method achieves comparable performances over the state-of-the-art methods. We further demonstrate the generalization of our method by experimenting on KITTI dataset.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 62076070) and the Science and Technology Innovation Action Plan of Shanghai (No. 23S41900400).

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Author notes

  1. These authors contributed equally to this work.

Authors and Affiliations

  1. Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China

    Mingzhi Yuan, Kexue Fu, Zhihao Li & Manning Wang

  2. Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention, Shanghai, 200032, China

    Mingzhi Yuan, Kexue Fu, Zhihao Li & Manning Wang

Authors
  1. Mingzhi Yuan
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  2. Kexue Fu
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  3. Zhihao Li
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  4. Manning Wang
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Corresponding author

Correspondence to Manning Wang.

Additional information

Mingzhi Yuan received the BS degrees in communication engineering from the Harbin Institute of Technology (HIT), China in 2020. He is currently a PhD student in School of Basic Medical Science of Fudan University, China. His research interests include 3D vision and medical image processing.

Kexue Fu received the BS degrees in Electronic and Information Engineering from Wuhan University of Technology, China in 2014. He is currently a PhD Candidate in School of Basic Medical Science of Fudan University, China. His research interest is computer vision and image-guided intervention.

Zhihao Li received the BS degrees in electronic information engineering from Wuhan University of Technology, China in 2021. He is currently a graduate student in School of Basic Medical Science of Fudan University, China. His research interests include point cloud registration and 3D vision.

Manning Wang received the BS and MS degrees in power electronics and power transmission from Shanghai Jiao Tong University, China in 1999 and 2002, respectively. He received PhD in biomedical engineering from Fudan University, China in 2011. He is currently a professor of biomedical engineering in School of Basic Medical Science of Fudan University, China. His research interests include medical image processing, image-guided intervention and computer vision.

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Yuan, M., Fu, K., Li, Z. et al. Decoupled deep hough voting for point cloud registration. Front. Comput. Sci. 18, 182703 (2024). https://doi.org/10.1007/s11704-023-2471-8

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