At present, view-based 3D model classification has the problems of insufficient visual information for single view and redundant information for multiple views, and treating all views equally will ignore the differences between different projection angles. To solve the above problems, a 3D model classification method based on Shannon entropy representative feature and voting mechanism is proposed. Firstly, multiple angle groups are set uniformly around 3D model, and multiple view sets representing the model are obtained. In order to extract effectively deep features from view, channel attention mechanism is introduced into the feature extraction network. Secondly, based on view discriminative features output from Softmax function, Shannon entropy is used to select representative feature for avoiding redundant feature of multiple views. Finally, based on representative features from multiple angle groups, voting mechanism is used to classify 3D model. Experiments show that the classification accuracy of the proposed method on 3D model dataset ModelNet10 reaches 96.48％, and classification performance is outstanding.
关键词
3维模型分类/注意力机制/香农熵代表性特征/投票机制
Key words
3D model classification/Attention mechanism/Shannon entropy representative feature/Voting mechanism
维普
万方数据