Fine-Grained 3D Model Classification Based on Deep Ensemble and Detail Awareness

Deep learning based 3D model classification methods have poor effectiveness in fine-grained 3D model classification.Aiming at the problem,an end-to-end fine-grained 3D model classification framework is proposed,and a network based on deep ensemble learning network and context detail awareness module(CDAM)is constructed.Inputting the multiply views of a 3D model,the global shape features are extracted through the deep ensemble learning sub-network.And the local detail features are obtained through the auxiliary sub-network based on CDAM.Based on above two sub-networks,an end-to-end weakly supervision fine-grained 3D model classification network is constructed.Experiments are conducted on three sub-datasets with different levels of difficulties,Airplane,Chair and Car,from the public dataset FG3D.The classification accuracies for above three sub-datasets are 96.31%,85.44%and 79.62%respectively,which demonstrate the fine classification performance and more generalization of the proposed method.