Multi-view Fusion 3D Model Classification - GAO Yuan; DING Bo; HE Yong-jun (Journal of Harbin University of Science and Technology)
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%.