Spnet: Deep 3D Object Classification And Retrieval Using Stereographic Projection

Abstract

We propose an efficient Stereographic Projection Neural Network (SPNet) for learning representations of 3D objects. We first transform a 3D input volume into a 2D planar image using stereographic projection. We then present a shallow 2D convolutional neural network (CNN) to estimate the object category followed by view ensemble, which combines the responses from multiple views of the object to further enhance the predictions. Specifically, the proposed approach 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 while having substantially lower GPU memory as well as network parameters. Despite its lightness, the experiments on 3D object classification and shape retrievals demonstrate the high performance of the proposed method.

Stats

• citations26
• S2 citations—
• github stars0
• HF likes0
• heat score10.74
• arxiv keyyavartanoo2018spnet

Related papers

• View N-gram Network For 3D Object Retrieval (2019)13.05
• Multiple Discrimination And Pairwise CNN For View-based 3D Object Retrieval (2020)14.27
• 3D Pose Estimation And 3D Model Retrieval For Objects In The Wild (2018)15.25
• Pvrnet: Point-view Relation Neural Network For 3D Shape Recognition (2018)13.11
• Deepsim-nets: Deep Similarity Networks For Stereo Image Matching (2023)5.24
• Extending Deepsdf For Automatic 3D Shape Retrieval And Similarity Transform Estimation (2020)0.00
• Risa-net: Rotation-invariant Structure-aware Network For Fine-grained 3D Shape Retrieval (2020)5.48
• Deeppoint3d: Learning Discriminative Local Descriptors Using Deep Metric Learning On 3D Point Clouds (2019)9.59