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

Abstract

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.