Correspondence Calculation of Three-Dimensional Point Cloud Model Based on Attention Mechanism
The existing deep learning methods have low precision and poor generalization ability in calculating dense correspondence between non-rigid point cloud models.To address these issues,a novel method for calculating unsupervised three-dimensional(3D)point cloud model correspondence based on a feature sequence attention mechanism was proposed.Firstly,the feature extraction module was used to extract the features of the input point cloud model pair.Secondly,the transformer module learned context information by capturing self-attention and cross-attention and generated a soft mapping matrix through the correspondence prediction module.Finally,the reconstruction module reconstructed the point cloud model based on the obtained soft mapping matrix and used the unsupervised loss function to complete training.The experimental results on FAUST,SHREC'19,and SMAL datasets show that the average correspondence errors of this algorithm are 5.1,5.8,and 5.4,respectively,which are lower than those of the classical algorithms including 3D-CODED,Elementary Structures,and CorrNet3D.The correspondence between non-rigid 3D point cloud models calculated by the proposed algorithm has higher accuracy and stronger generalization ability.
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