The integration of view-based 3D model classification and deep learning can effectively improve the classification accuracy. However, current methods consider that the views from different viewpoints of 3D model with same category belong to the same category and ignore the view differences, which makes it difficult for the classifier to learn a reasonable classification surface. To solve this problem, a 3D model classification method based on deep neural network is proposed. The multiple viewpoint groups are set evenly around the 3D model in this method, and the view classifier for each viewpoint group is trained for fully mining the deep information of the 3D model in different viewpoint groups. These classifiers share a feature extraction network, but have their own classification network. In order to extract the discriminative view features, the attention mechanism is added to the feature extraction network; In order to model the views of the non-viewpoint group, additional classes are added to the classification network. In the classification stage, a view selection strategy is first proposed, which can use a small number of views to classify the 3D model and improve classification efficiency. Then a classification strategy is proposed to achieve reliable 3D model classification through classification view. Experimental results on ModelNet10 and ModelNet40 show that the classification accuracy can reach up to 93.6% and 91.0% with only 3 views.

At present, view-based 3D model classification is a research hotspot. However, current methods produce many redundant views, and all views are treated equally, ignoring their differences and importance. To solve the above problems, we propose a multi-view fusion 3D model classification method. This method first extracts view features using the view feature extraction network with mixed domain attention, and then fuses these view features and inputs the fused features into the view weight learning network with channel attention, giving different weights to different views according to their importance to the 3D model, and forming representative feature descriptors for 3D model classification. Experimental results shows that the classification accuracy rates in the rigid 3D model data sets ModelNet10 and ModelNet40 reached 98.3% and 95.5%.