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We propose an efficient Stereographic Projection Neural Network (SPNet) forlearning representations of 3D objects. We first transform a 3D input volumeinto a 2D planar image using stereographic projection. We then present ashallow 2D convolutional neural network (CNN) to estimate the object categoryfollowed by view ensemble, which combines the responses from multiple views ofthe object to further enhance the predictions. Specifically, the proposedapproach consists of four stages: (1) Stereographic projection of a 3D object,(2) view-specific feature learning, (3) view selection and (4) view ensemble.The proposed approach performs comparably to the state-of-the-art methods whilehaving substantially lower GPU memory as well as network parameters. Despiteits lightness, the experiments on 3D object classification and shape retrievalsdemonstrate the high performance of the proposed method.

SPNet: Deep 3D Object Classification and Retrieval using Stereographic Projection