3D Model Classification and Retrieval Based on CNN and Voting Scheme

The existing deep learning algorithms for view-based 3D model classification use pixel-level operations,such as maximum pooling and average pooling,to fuse the views’information,which may lose or overwrite the useful information of 3D models.Aiming at the problem,a 3D model classification and retrieval algorithm based on convolutional neural network and voting scheme is proposed.Firstly,each 3D model is converted to a set of 2D views.Then,those 2D views are classified based on deep learning model CaffeNet with rich digital image library ImageNet.Finally,the 3D model is classified by weighted voting.Furthermore,based on the voting scheme,four distance measurement algorithms are proposed to retrieve 3D model.Experiments on the rigid 3D model libraries ModelNet10,ModelNet40,and the non-rigid 3D model libraries SHREC10,SHREC11 and SHREC15 demonstrate the effectiveness of the proposed algorithm.The classification accuracy rates for above five libraries are 93.18%,93.07%,99.5%,99.5%and 99.4%respectively,and the retrieval performance is on par or better than state-of-the-art methods.