Advertisement

3D ResNets for 3D Object Classification

  • Anastasia Ioannidou
  • Elisavet Chatzilari
  • Spiros Nikolopoulos
  • Ioannis Kompatsiaris
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11295)

Abstract

During the last few years, deeper and deeper networks have been constantly proposed for addressing computer vision tasks. Residual Networks (ResNets) are the latest advancement in the field of deep learning that led to remarkable results in several image recognition and detection tasks. In this work, we modify two variants of the original ResNets, i.e. Wide Residual Networks (WRNs) and Residual of Residual Networks (RoRs), to work on 3D data and investigate for the first time, to our knowledge, their performance in the task of 3D object classification. We use a dataset containing volumetric representations of 3D models so as to fully exploit the underlying 3D information and present evidence that ‘3D ResNets’ constitute a valuable tool for classifying objects on 3D data as well.

Keywords

3D object classification 3D object recognition Deep learning Residual networks 

Notes

Acknowledgements

The research leading to these results has received funding from the European Union H2020 Horizon Programme (2014–2020) under grant agreement 665066, project DigiArt (The Internet Of Historical Things And Building New 3D Cultural Worlds).

References

  1. 1.
    Brock, A., Lim, T., Ritchie, J., Weston, N.: Generative and discriminative voxel modeling with convolutional neural networks. CoRR abs/1608.04236 (2016). http://arxiv.org/abs/1608.04236
  2. 2.
    Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. In: British Machine Vision Conference (BMVC) (2014)Google Scholar
  3. 3.
    Chollet, F., et al.: Keras (2015). https://github.com/fchollet/keras
  4. 4.
    Gu, J., et al.: Recent advances in convolutional neural networks. Pattern Recognit. 77, 354–377 (2018).  https://doi.org/10.1016/j.patcog.2017.10.013CrossRefGoogle Scholar
  5. 5.
    He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: IEEE International Conference on Computer Vision ICCV, pp. 1026–1034 (2015)Google Scholar
  6. 6.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)Google Scholar
  7. 7.
    He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46493-0_38CrossRefGoogle Scholar
  8. 8.
    Hegde, V., Zadeh, R.: FusionNet: 3D object classification using multiple data representations. CoRR abs/1607.05695 (2016).http://arxiv.org/abs/1607.05695
  9. 9.
    Huang, G., Sun, Y., Liu, Z., Sedra, D., Weinberger, K.Q.: Deep networks with stochastic depth. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 646–661. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46493-0_39CrossRefGoogle Scholar
  10. 10.
    Ioannidou, A., Chatzilari, E., Nikolopoulos, S., Kompatsiaris, I.: Deep learning advances in computer vision with 3D data: a survey. ACM Comput. Surv. 50(2), 201–2038 (2017).  https://doi.org/10.1145/3042064CrossRefGoogle Scholar
  11. 11.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network trainingby reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning (ICML), pp. 448–456 (2015).http://jmlr.org/proceedings/papers/v37/ioffe15.html
  12. 12.
    Johns, E., Leutenegger, S., Davison, A.: Pairwise decomposition of image sequences for active multi-view recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3813–3822 (2016)Google Scholar
  13. 13.
    Kingma, D., Ba, J.: Adam: a method for stochastic optimization. CoRR abs/1412.6980 (2014). http://arxiv.org/abs/1412.6980
  14. 14.
    Klokov, R., Lempitsky, V.: Escape from cells: deep Kd-networks for the recognition of 3D point cloud models. CoRR abs/1704.01222 (2017). http://arxiv.org/abs/1704.01222
  15. 15.
    Maturana, D., Scherer, S.: VoxNet: a 3D convolutional neural network forreal-time object recognition. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 922–928 (2015)Google Scholar
  16. 16.
    Moniz, J., Pal, C.: Convolutional residual memory networks. CoRR abs/1606.05262 (2016). http://arxiv.org/abs/1606.05262
  17. 17.
    Qi, C., Su, H., Nießner, M., Dai, A., Yan, M., Guibas, L.: Volumetric and multi-view CNNs for object classification on 3D data. CoRR abs/1604.03265 (2016). http://arxiv.org/abs/1604.03265
  18. 18.
    Sedaghat, N., Zolfaghari, M., Brox, T.: Orientation-boosted voxel nets for 3D object recognition. CoRR abs/1604.03351 (2016). http://arxiv.org/abs/1604.03351
  19. 19.
    Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)MathSciNetzbMATHGoogle Scholar
  20. 20.
    Srivastava, R., Greff, K., Schmidhuber, J.: Highway networks. CoRR abs/1505.00387 (2015). http://arxiv.org/abs/1505.00387
  21. 21.
    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 (ICCV), pp. 945–953 (2015)Google Scholar
  22. 22.
    Targ, S., Almeida, D., Lyman, K.: Resnet in resnet: generalizing residual architectures. CoRR abs/1603.08029 (2016).http://arxiv.org/abs/1603.08029
  23. 23.
    Theano Development Team: Theano: A Python framework for fast computation of mathematical expressions. arXiv e-prints abs/1605.02688, May 2016. http://arxiv.org/abs/1605.02688
  24. 24.
    Wu, Z., et al.: 3D ShapeNets: a deep representation for volumetric shapes. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015Google Scholar
  25. 25.
    Zagoruyko, S., Komodakis, N.: Wide residual networks. In: BMVC (2016)Google Scholar
  26. 26.
    Zhang, K., Sun, M., Han, X., Yuan, X., Guo, L., Liu, T.: Residual networks of residual networks: multilevel residual networks. IEEE Trans. Circ. Syst. Video Technol. PP(99), 1 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Anastasia Ioannidou
    • 1
  • Elisavet Chatzilari
    • 1
  • Spiros Nikolopoulos
    • 1
  • Ioannis Kompatsiaris
    • 1
  1. 1.Information Technologies Institute, Centre for Research and Technology HellasThermiGreece

Personalised recommendations