Panorama based on multi-channel-attention CNN for 3D model recognition


With the development of 3D model reconstruction, manufacturing, and 3D model vision technologies, 3D model recognition has attracted much attention recently. To handle the 3D model recognition problem, in this paper, we propose a panorama based on multi-channel-attention (MCA) CNN network for the representation of the 3D model. The proposed method is composed of three parts: extracting views, transform function learning, and generating 3D model descriptor. Concretely, we first extract the 2D panoramic views for each 3D model, and we use the multi-channel-attention neural network to extract the descriptor for each 3D model. Here, the attention model is used to find the unequal weights of each panorama view to generate the more robust 3D model descriptor. Finally, The fusion feature is used to handle the 3D model classification and retrieval problem. The popular data sets ModelNet and ShapeNet are used to demonstrate the performance of our approach. The experiments also demonstrate the superiority of our proposed method over the state-of-art methods.

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This work was supported in part by the National Natural Science Foundation of China (61502337,61872267).

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Correspondence to Qi Liang or Roubing He.

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Communicated by X. He, H. Zhang, Z. Liu, C. Ngo, S. Karaman, Y. Zhang.

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Nie, W., Wang, K., Liang, Q. et al. Panorama based on multi-channel-attention CNN for 3D model recognition. Multimedia Systems 25, 655–662 (2019).

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  • Panorama view
  • 3D Model retrieval
  • 3D Model classification
  • Multi-channel CNN
  • Attention