3D Shape Classification Method Based on Shape-Aware Deep Learning

In order to solve the problem of complex and massive 3D shapes recognition,we propose a structure-aware deep learning model for 3D shape classification.By learning the geometric structure and spatial structure jointly,we generate a structure-aware deep feature vector,which has stronger discriminative ability and stability on 3D shape classification.Firstly,optimal multi-scale HKS features are extracted to construct discerning geometric shape descriptor based on a CNN learning model.Secondly,a multi-view based CNN learning framework is built to extract spatial structure feature.Finally,all the features are jointly learned and generate a structure-aware feature vector.We explore our method by using C++,Matlab,TensorFlow platform,a serial of experimental analysis are carried out in the public 3D databases,they have shown that our deep features are stable for complex topological structure and large-scale geometric deformation.Compared with related methods,this method has higher classification accuracy in 3D shape classification.