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The assessment of 3D model representation for retrieval with CNN-RNN networks

  • Weizhi NieEmail author
  • Kun Wang
  • Hongtao Wang
  • Yuting SuEmail author
Article
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Abstract

In this paper, we propose a novel method for assessing 3D model representation via CNN and RNN networks. First, a visual tool developed with OpenGL is utilized to extract virtual views of each 3D model from different angles. These views are extracted by 10-degree wrap around the model. Second, a CNN model is used to extract the feature vectors of these virtual images. Then, these feature vectors as the input of an RNN are fused into a new feature to represent the 3D model. Finally, the Euclidean distance is used to obtain the similarity measure between two different models for the retrieval problem. In the experimental section, NTU, PSB and ShapeNet datasets are utilized to evaluate the performance of the proposed method. Several classic 3D model retrieval and classification methods are leveraged as comparison methods in this paper. The corresponding experiments also demonstrate the superiority of our approach.

Keywords

3D model retrieval RNN Deep learning Information retrieval 

Notes

Acknowledgements

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

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electrical and Information EngineeringTianjin UniversityTianjinChina

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