Multi-guided Point Cloud Registration Network Combined with Attention Mechanism
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-dimen-sional features of the two unaligned point clouds and then the Transformer is used to fuse the independent features with the con-textual 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 vec-tor in ModelNet40 target unknown experiments is reduced to 0.025 and 0.004 6,respectively.AMGNet alignment has high accu-racy,high interference resistance,and good generalization ability.
Point cloud registrationAttention mechanismMultiple matrix guidanceWeighted SVD
万方数据
维普
