Recently

3D model retrieval based on views has become a research hotspot. In this method

3D models are represented as a collection of 2D views

which allows deep learning techniques to be used for 3D model classification and retrieval. However

current methods need improvements on both accuracy and efficiency. We propose a 3D model retrieval method

which includes index building and model retrieval. In the index building stage

representative views are selected and input into a well-learned Convolutional Neural Network (CNN) for feature extraction and classification. Next

the features are organized according to their labels to build indexes. In the retrieval stage

the representative views of the input model are classified into a category with the CNN and voting algorithm

and then only the features of one category rather than all categories are chosen to perform similarity matching. In this way

the searching space for retrieval is reduced. In addition

the number of the used views for retrieval is gradually increased. Once there is enough evidence to determine a 3D model

the retrieval process will be terminated ahead of time. Experiments on the rigid 3D model datasets ModelNet10

ModelNet40

and the non-rigid 3D model dataset McGill10 show that the proposed method can improve the retrieval efficiency substantially while keeping high retrieval accuracy rates at 94%

92% and 100%

respectively.