Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (2): 142-150.doi: 10.11896/jsjkx.230200073

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Multi-guided Point Cloud Registration Network Combined with Attention Mechanism

LIU Xuheng, BAI Zhengyao, XU Zhu, DU Jiajin, XIAO Xiao   

  1. School of Information Science and Engineering,Yunnan University,Kunming 650221,China
  • Received:2023-02-20 Revised:2023-06-20 Online:2024-02-15 Published:2024-02-22
  • About author:LIU Xuheng,born in 1998,postgra-duate.His main research interests include point cloud registration and three-dimensional reconstruction.BAI Zhengyao,born in 1967,Ph.D,professor,master supervisor.His main research interests include signal proces-sing,image processing,pattern recognition and machine learning,etc.
  • Supported by:
    Yunnan Provincial Major Science and Technology Special Plan Projects(202002AD080001) and 14th Postgra-duate Research Innovation Project of Yunnan University(KC-22222543).

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Abstract: This paper proposes a point cloud alignment network,AMGNet,which uses the probability matrix of matching points between point clouds and the spatial information feature matrix of point clouds to search for correspondence and determine the weights of corresponding points with each other.First,the point cloud feature extraction network is used to get the high-dimensional features of the two unaligned point clouds and then the Transformer is used to fuse the independent features with the contextual information.Also,the weight assignment uses the strategy of double matrix co-determination.Finally,the singular value decomposition is used to obtain the required rigid transformation matrix.Several experiments are conducted on synthetic datasets,such as ModelNet40,7Scenes and real scenes.The results show that the mean square error of rotation matrix and translation vector in ModelNet40 target unknown experiments is reduced to 0.025 and 0.004 6,respectively.AMGNet alignment has high accuracy,high interference resistance,and good generalization ability.

Key words: Point cloud registration, Attention mechanism, Multiple matrix guidance, Weighted SVD

CLC Number: 

  • TP391.41
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