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

  • Weizhi Nie
  • Kun Wang
  • Qi LiangEmail author
  • Roubing HeEmail author
Special Issue Paper
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Abstract

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.

Keywords

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

Notes

Acknowledgements

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

References

  1. 1.
    Jingyuan, C., Hanwang, Z., Xiangnan, H., Liqiang, N., Wei L., Tat Seng, C.: Attentive collaborative filtering: Multimedia recommendation with item- and component-level attention. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 335–344 (2017)Google Scholar
  2. 2.
    He, X., Chua, T.-S.: Neural factorization machines for sparse predictive analytics. In: Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 355–364. ACM (2017)Google Scholar
  3. 3.
    Zhang, H., Niu, Y., Chang, S.-F.: Grounding referring expressions in images by variational context (2018)Google Scholar
  4. 4.
    Kanezaki, A., Matsushita, Y., Yoshifumi, N.: RotationNet: joint object categorization and pose estimation using multiviews from unsupervised viewpoints. CVPR, pp. 5010–5019 (2018)Google Scholar
  5. 5.
    Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.: Multi-view convolutional neural networks for 3d shape recognition. In: Proceedings of the IEEE international conference on computer vision, pp. 945–953 (2015)Google Scholar
  6. 6.
    Liu, A.-A., Nie, W.-Z., Su, Y.-T.: 3d object retrieval based on multi-view latent variable model. In: IEEE Transactions on Circuits Systems for Video Technology. (99):1–1Google Scholar
  7. 7.
    Guo, H., Wang, J., Gao, Y., Li, J., Lu, H.: Multi-view 3d object retrieval with deep embedding network. In: IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society 25(12), 5526–5537 (2016)Google Scholar
  8. 8.
    Wang, D., Wang, B., Zhao, S., Yao, H., Liu, H.: View-based 3d object retrieval with discriminative views. Neurocomputing 252, 58–66 (2017)CrossRefGoogle Scholar
  9. 9.
    Zhang, H., Kyaw, Z., Jinyang, Y., Shih F.C.: Weakly supervised visual relation detection via parallel pairwise r-fcn, Ppr-fcn (2017)Google Scholar
  10. 10.
    Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.G.: Multi-view convolutional neural networks for 3d shape recognition. ICCV, pp. 945–953 (2015)Google Scholar
  11. 11.
    Papadakis, Panagiotis, Pratikakis, Ioannis, Theoharis, Theoharis, Perantonis, Stavros: Panorama: a 3d shape descriptor based on panoramic views for unsupervised 3d object retrieval. Int. J. Comput. Vis. 89(2–3), 177–192 (2010)CrossRefGoogle Scholar
  12. 12.
    Zhang, H., Kyaw, Z., Chang, S.-F., Chua, T.-S.: Visual translation embedding network for visual relation detection. CVPR, pp. 3107–3115 (2017)Google Scholar
  13. 13.
    Liu, A., Wang, Z., Nie, W., Yuting, S.: Graph-based characteristic view set extraction and matching for 3d model retrieval. Inf. Sci. 320, 429–442 (2015)CrossRefGoogle Scholar
  14. 14.
    Yang, Luren, Albregtsen, Fritz: Fast and exact computation of cartesian geometric moments using discrete green’s theorem. Pattern Recogn. 29(7), 1061–1073 (1996)CrossRefGoogle Scholar
  15. 15.
    Ke, L., Wang, Q., Xue, J., Pan, W.: 3d model retrieval and classification by semi-supervised learning with content-based similarity. Inf. Sci. 281, 703–713 (2014)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Polewski, P., Yao, W., Heurich, M., Krzystek, P., Stilla, U.: Detection of fallen trees in als point clouds of a temperate forest by combining point/primitive-level shape descriptors. Gemeinsame Tagung (2014)Google Scholar
  17. 17.
    Kobbelt, L., Schrder, P., Kazhdan, M., Funkhouser, T., Rusinkiewicz, S.: Rotation invariant spherical harmonic representation of 3d shape descriptors. In: Proc. eurographics/acm Siggraph Symp.on Geometry Processing 43(2), 156–164 (2003)Google Scholar
  18. 18.
    Sinha, Y., Bai, J., Ramani, K.: Deep learning 3d shape surfaces using geometry images. In: European Conference on Computer Vision, pp. 223–240 (2016)Google Scholar
  19. 19.
    Nie, Wei-Zhi, Liu, An-An, Yu-Ting, Su: 3d object retrieval based on sparse coding in weak supervision. J. Vis. Commun. Image Represent. 37, 40–45 (2016)CrossRefGoogle Scholar
  20. 20.
    He, X., He, Z., Du, X., Chua, T.-S.: Adversarial personalized ranking for recommendation (2018)Google Scholar
  21. 21.
    He, X., He, Z., Song, J., Liu, Z., Jiang, Y.-G., Chua, T.-S.: NAIS: neural attentive item similarity model for recommendation. IEEE Trans. Knowl. Data Eng. 30(12), 2354–2366 (2018)CrossRefGoogle Scholar
  22. 22.
    Ding-Yun, C., Xiao-Pei, T., Yu-Te, S., Ming, O.: On visual similarity based 3d model retrieval. In: Computer graphics forum, 22, pp. 223–232. Wiley Online Library (2003)Google Scholar
  23. 23.
    Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: 3d shapenets: a deep representation for volumetric shapes. pp. 1912–1920 (2014)Google Scholar
  24. 24.
    Maturana, D., Scherer, S.: Voxnet: A 3d convolutional neural network for real-time object recognition. In: Intelligent Robots and Systems (IROS), 2015. In: IEEE/RSJ International Conference on, pp. 922–928. IEEE (2015)Google Scholar
  25. 25.
    Shi, Baoguang, Bai, Song, Zhou, Zhichao, Bai, Xiang: Deeppano: deep panoramic representation for 3-d shape recognition. IEEE Signal Process. Lett. 22(12), 2339–2343 (2015)CrossRefGoogle Scholar
  26. 26.
    Sedaghat, N., Zolfaghari, M., Amiri, E., Brox, T.: Orientation-boosted voxel nets for 3d object recognition. arXiv preprint arXiv:1604.03351 (2016)
  27. 27.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  28. 28.
    Srivastava, Nitish, Hinton, Geoffrey, Krizhevsky, Alex, Sutskever, Ilya, Salakhutdinov, Ruslan: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)MathSciNetzbMATHGoogle Scholar
  29. 29.
    Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: 3d shapenets: a deep representation for volumetric shapes. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1912–1920 (2015)Google Scholar
  30. 30.
    Sfikas, K., Theoharis, T., Pratikakis, I.: Exploiting the panorama representation for convolutional neural network classification and retrieval. In: Eurographics Workshop on 3D Object Retrieval (2017)Google Scholar
  31. 31.
    Song, B., Xiang, B., Zhichao, Z., Zhaoxiang, Z., Longin Jan, L.: Gift: A real-time and scalable 3d shape search engine. In: Computer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference on, pp. 5023–5032. IEEE (2016)Google Scholar
  32. 32.
    Sedaghat, N., Zolfaghari, M., Brox, T.: Orientation-boosted voxel nets for 3d object recognition (2017)Google Scholar
  33. 33.
    Wu, J., Zhang, C., Xue, T., Freeman, B., Tenenbaum, J.: Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling. In: Advances in Neural Information Processing Systems, pp. 82–90 (2016)Google Scholar
  34. 34.
    Alberto, G.-G., Francisco, G.-D., Jose, G.-R., Sergio, O.-E., Miguel, C., Azorin-Lopez, J.: Pointnet: A 3d convolutional neural network for real-time object class recognition. In: Neural Networks (IJCNN), 2016 International Joint Conference on, pp. 1578–1584. IEEE (2016)Google Scholar
  35. 35.
    Xu, X., Todorovic, S.: Beam search for learning a deep convolutional neural network of 3d shapes. In: ICPR, pp. 3506–3511 (2016)Google Scholar
  36. 36.
    Savva, M., Yu, F., Su, H., Aono, M., Chen, B., Cohen-Or, D., Deng, W., Su, H., Bai, S., Bai, X., et al.: Large-scale 3D shape retrieval from ShapeNet core55[C]// Eurographics Workshop on 3d Object Retrieval. Eurographics Association (2016)Google Scholar
  37. 37.
    Takahiko, F., Ryutarou, O.: Deep aggregation of local 3d geometric features for 3d model retrieval. In: BMVC (2016)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.The School of Electrical and Information EngineeringTianjin UniversityTianjinChina

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