Three-dimensional model feature extraction based on attention mechanism
Currently,in view-based 3D model retrieval technology,most methods for multi-view feature extraction focus on the global feature information of the view and ignore the exploration of the correlation between the local feature information of the view and the multi-view.To solve this problem,a new feature extraction method is proposed,which uses the convolutional neural net-work in deep learning and combines the attention mechanism to extract features to improve its discriminability.The method con-ducts experimental analysis on ModelNet40,takes multiple views of the three-dimensional model as input,and adds an attention module to the network layer for feature extraction and classification.The results show that this method is superior to existing typical algorithms in terms of classification accuracy.
NETL
NSTL
万方数据
维普
